diff --git a/.cursor/commands/2.planning/2.1_gen_components.md b/.cursor/commands/2.planning/2.10_gen_components.md similarity index 83% rename from .cursor/commands/2.planning/2.1_gen_components.md rename to .cursor/commands/2.planning/2.10_gen_components.md index 660dbee..ff91dab 100644 --- a/.cursor/commands/2.planning/2.1_gen_components.md +++ b/.cursor/commands/2.planning/2.10_gen_components.md @@ -44,6 +44,8 @@ - Generate draw.io components diagram shows relations between components. ## Notes - Components should be semantically coherents. Do not spread similar functionality across multiple components - Do not put any code yet, only names, input and output. - Ask as many questions as possible to clarify all uncertainties. \ No newline at end of file + - Strongly follow Single Responsibility Principle during creation of components. + - Follow dumb code - smart data principle. Do not overcomplicate + - Components should be semantically coherents. Do not spread similar functionality across multiple components + - Do not put any code yet, only names, input and output. + - Ask as many questions as possible to clarify all uncertainties. \ No newline at end of file diff --git a/.cursor/commands/2.planning/2.15_components_assesment.md b/.cursor/commands/2.planning/2.15_components_assesment.md new file mode 100644 index 0000000..45c4245 --- /dev/null +++ b/.cursor/commands/2.planning/2.15_components_assesment.md @@ -0,0 +1,25 @@ +# component assesment + +## Problem statement + - @00_problem + +## Solution and decomposition + - @docs/01_solution/solution.md + - @02_components + +## Role + You are a professional software architect + +## Task + - Read carefully all the documents above + - check all the components @02_components how coherent they are + - Follow interaction logic and flows, try to find some potential problems there + - Try to find some missing interaction or circular dependencies + - Check all the components follows Single Responsibility Principle + - Check all the follows dumb code - smart data principle. So that resulting code shouldn't be overcomplicated + +## Output + Form a list of problems with fixes in the next format: + - Component + - Problem, reason + - Fix or potential fixes \ No newline at end of file diff --git a/.cursor/commands/2.planning/2.2_gen_epics.md b/.cursor/commands/2.planning/2.20_gen_epics.md similarity index 100% rename from .cursor/commands/2.planning/2.2_gen_epics.md rename to .cursor/commands/2.planning/2.20_gen_epics.md diff --git a/.cursor/commands/2.planning/2.3_gen_tests.md b/.cursor/commands/2.planning/2.30_gen_tests.md similarity index 100% rename from .cursor/commands/2.planning/2.3_gen_tests.md rename to .cursor/commands/2.planning/2.30_gen_tests.md diff --git a/.cursor/commands/2.planning/2.4_gen_features.md b/.cursor/commands/2.planning/2.40_gen_features.md similarity index 100% rename from .cursor/commands/2.planning/2.4_gen_features.md rename to .cursor/commands/2.planning/2.40_gen_features.md diff --git a/docs/02_components/02_flight_processor/flight_processor_spec.md b/docs/02_components/02_flight_processor/flight_processor_spec.md index 1abe2ba..eeacaee 100644 --- a/docs/02_components/02_flight_processor/flight_processor_spec.md +++ b/docs/02_components/02_flight_processor/flight_processor_spec.md @@ -148,9 +148,10 @@ flight_id: str # UUID 3. Validate rough_waypoints via validate_waypoint() 4. Get flight configuration from F17 Configuration Manager 5. Initialize flight state -6. Trigger F04 Satellite Data Manager → prefetch_route_corridor() -7. Save flight to F03 Flight Database -8. Return flight_id +6. **Set ENU origin**: Call F13 Coordinate Transformer → set_enu_origin(flight_id, start_gps) +7. Trigger F04 Satellite Data Manager → prefetch_route_corridor() +8. Save flight to F03 Flight Database +9. Return flight_id **Error Conditions**: - `ValidationError`: Invalid waypoints or geofences @@ -586,8 +587,7 @@ FrameResult: 5. Else: - Pre-rotate image to current heading 6. Compute relative pose via F07 Sequential VO (chunk-aware) -7. Add relative factor to chunk via F10.add_relative_factor_to_chunk() -8. Add frame to chunk via F12 Route Chunk Manager.add_frame_to_chunk() +7. Add frame to chunk via F12 Route Chunk Manager.add_frame_to_chunk() (F12 handles F10 internally) 9. Get satellite tile from F04 Satellite Data Manager 10. Align to satellite via F09 Metric Refinement (with tile_bounds) 11. If alignment successful: @@ -953,7 +953,7 @@ bool: True if initialization successful - **F10 Factor Graph Optimizer**: Trajectory optimization and chunk management - **F11 Failure Recovery Coordinator**: Tracking loss handling and chunk matching - **F12 Route Chunk Manager**: Chunk lifecycle management -- **F13 Coordinate Transformer**: Pixel to GPS conversion +- **F13 Coordinate Transformer**: Pixel to GPS conversion and ENU coordinate management - **F14 Result Manager**: Result publishing - **F15 SSE Event Streamer**: Real-time streaming - **F16 Model Manager**: ML model loading diff --git a/docs/02_components/03_flight_database/flight_database_spec.md b/docs/02_components/03_flight_database/flight_database_spec.md index cf1487b..dda2b22 100644 --- a/docs/02_components/03_flight_database/flight_database_spec.md +++ b/docs/02_components/03_flight_database/flight_database_spec.md @@ -93,6 +93,19 @@ class IFlightDatabase(ABC): @abstractmethod def get_image_metadata(self, flight_id: str, frame_id: int) -> Optional[Dict]: pass + + # Chunk State Operations + @abstractmethod + def save_chunk_state(self, flight_id: str, chunk: ChunkHandle) -> bool: + pass + + @abstractmethod + def load_chunk_states(self, flight_id: str) -> List[ChunkHandle]: + pass + + @abstractmethod + def delete_chunk_state(self, flight_id: str, chunk_id: str) -> bool: + pass ``` ## Component Description @@ -342,7 +355,7 @@ waypoint_id: str **Called By**: - F02 Flight Processor -- F13 Result Manager +- F14 Result Manager **Input**: ```python @@ -475,7 +488,7 @@ Optional[FlightState] **Description**: Saves frame processing result. **Called By**: -- F13 Result Manager +- F14 Result Manager **Input**: ```python @@ -505,7 +518,7 @@ bool: True if saved **Description**: Gets all frame results for flight. **Called By**: -- F13 Result Manager +- F14 Result Manager **Test Cases**: 1. Get results → returns all frames @@ -646,6 +659,100 @@ Optional[Dict]: Metadata dictionary or None --- +## Chunk State Operations + +### `save_chunk_state(flight_id: str, chunk: ChunkHandle) -> bool` + +**Description**: Saves chunk state to database for crash recovery. + +**Called By**: +- F12 Route Chunk Manager (after chunk state changes) + +**Input**: +```python +flight_id: str +chunk: ChunkHandle: + chunk_id: str + start_frame_id: int + end_frame_id: Optional[int] + frames: List[int] + is_active: bool + has_anchor: bool + anchor_frame_id: Optional[int] + anchor_gps: Optional[GPSPoint] + matching_status: str +``` + +**Output**: +```python +bool: True if saved successfully +``` + +**Database Operations**: +```sql +INSERT INTO chunks (chunk_id, flight_id, start_frame_id, end_frame_id, frames, + is_active, has_anchor, anchor_frame_id, anchor_lat, anchor_lon, + matching_status, updated_at) +VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, NOW()) +ON CONFLICT (chunk_id) UPDATE SET ... +``` + +**Test Cases**: +1. **Save new chunk**: Persisted successfully +2. **Update existing chunk**: State updated +3. **Multiple chunks**: All persisted correctly + +--- + +### `load_chunk_states(flight_id: str) -> List[ChunkHandle]` + +**Description**: Loads all chunk states for a flight (for crash recovery). + +**Called By**: +- F12 Route Chunk Manager (on flight resume) +- System startup (recovery) + +**Input**: +```python +flight_id: str +``` + +**Output**: +```python +List[ChunkHandle]: All chunks for the flight +``` + +**Test Cases**: +1. **Load chunks**: Returns all chunks +2. **No chunks**: Returns empty list +3. **Crash recovery**: Chunks restored correctly + +--- + +### `delete_chunk_state(flight_id: str, chunk_id: str) -> bool` + +**Description**: Deletes chunk state from database. + +**Called By**: +- F12 Route Chunk Manager (after chunk merged/deleted) + +**Input**: +```python +flight_id: str +chunk_id: str +``` + +**Output**: +```python +bool: True if deleted +``` + +**Test Cases**: +1. **Delete chunk**: Removed from database +2. **Non-existent chunk**: Returns False + +--- + ## Integration Tests ### Test 1: Complete Flight Lifecycle @@ -809,6 +916,27 @@ CREATE TABLE flight_images ( FOREIGN KEY (flight_id) REFERENCES flights(id) ON DELETE CASCADE, INDEX idx_images_flight (flight_id, frame_id) ); + +-- Chunks table +CREATE TABLE chunks ( + chunk_id VARCHAR(36) PRIMARY KEY, + flight_id VARCHAR(36) NOT NULL, + start_frame_id INT NOT NULL, + end_frame_id INT, + frames JSONB NOT NULL, + is_active BOOLEAN NOT NULL DEFAULT TRUE, + has_anchor BOOLEAN NOT NULL DEFAULT FALSE, + anchor_frame_id INT, + anchor_lat DECIMAL(10, 7), + anchor_lon DECIMAL(11, 7), + matching_status VARCHAR(50) NOT NULL DEFAULT 'unanchored', + created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (flight_id) REFERENCES flights(id) ON DELETE CASCADE, + INDEX idx_chunks_flight (flight_id), + INDEX idx_chunks_active (flight_id, is_active), + INDEX idx_chunks_matching (flight_id, matching_status) +); ``` --- diff --git a/docs/02_components/04_satellite_data_manager/satellite_data_manager_spec.md b/docs/02_components/04_satellite_data_manager/satellite_data_manager_spec.md index 1dfaea5..6618c3b 100644 --- a/docs/02_components/04_satellite_data_manager/satellite_data_manager_spec.md +++ b/docs/02_components/04_satellite_data_manager/satellite_data_manager_spec.md @@ -245,15 +245,16 @@ for tiles in progressive_fetch(lat, lon, [1, 4, 9, 16, 25], 19): --- -### `cache_tile(tile_coords: TileCoords, tile_data: np.ndarray) -> bool` +### `cache_tile(flight_id: str, tile_coords: TileCoords, tile_data: np.ndarray) -> bool` -**Description**: Caches a satellite tile to disk. +**Description**: Caches a satellite tile to disk with flight_id association. **Called By**: - Internal (after fetching tiles) **Input**: ```python +flight_id: str # Flight this tile belongs to tile_coords: TileCoords: x: int y: int @@ -267,10 +268,11 @@ bool: True if cached successfully ``` **Processing Flow**: -1. Generate cache key from tile_coords -2. Serialize tile_data (PNG format) -3. Write to disk cache directory -4. Update cache index +1. Generate cache path: `/satellite_cache/{flight_id}/{zoom}/{tile_x}_{tile_y}.png` +2. Create flight cache directory if not exists +3. Serialize tile_data (PNG format) +4. Write to disk cache directory +5. Update cache index with flight_id association **Error Conditions**: - Returns `False`: Disk write error, space full @@ -282,9 +284,9 @@ bool: True if cached successfully --- -### `get_cached_tile(tile_coords: TileCoords) -> Optional[np.ndarray]` +### `get_cached_tile(flight_id: str, tile_coords: TileCoords) -> Optional[np.ndarray]` -**Description**: Retrieves a cached tile from disk. +**Description**: Retrieves a cached tile from disk, checking flight-specific cache first. **Called By**: - Internal (before fetching from API) @@ -292,6 +294,7 @@ bool: True if cached successfully **Input**: ```python +flight_id: str # Flight to check cache for tile_coords: TileCoords ``` @@ -301,10 +304,11 @@ Optional[np.ndarray]: Tile image or None if not cached ``` **Processing Flow**: -1. Generate cache key -2. Check if file exists -3. Load and deserialize -4. Return tile_data +1. Generate cache path: `/satellite_cache/{flight_id}/{zoom}/{tile_x}_{tile_y}.png` +2. Check flight-specific cache first +3. If not found, check global cache (shared tiles) +4. If file exists, load and deserialize +5. Return tile_data or None **Error Conditions**: - Returns `None`: Not cached, corrupted file diff --git a/docs/02_components/05_image_input_pipeline/image_input_pipeline_spec.md b/docs/02_components/05_image_input_pipeline/image_input_pipeline_spec.md index 3b18491..25e0f5d 100644 --- a/docs/02_components/05_image_input_pipeline/image_input_pipeline_spec.md +++ b/docs/02_components/05_image_input_pipeline/image_input_pipeline_spec.md @@ -330,8 +330,8 @@ ImageMetadata: **Description**: Gets current processing status for a flight. **Called By**: -- F01 GPS-Denied REST API (status endpoint) -- F02 Flight Manager +- F01 Flight API (status endpoint) +- F02 Flight Processor **Input**: ```python @@ -349,6 +349,11 @@ ProcessingStatus: processing_rate: float # images/second ``` +**Processing Flow**: +1. Get flight state via F02 Flight Processor.get_flight_state(flight_id) +2. Combine with internal queue status +3. Return ProcessingStatus + **Test Cases**: 1. **Get status**: Returns accurate counts 2. **During processing**: Updates in real-time @@ -397,7 +402,8 @@ ProcessingStatus: ### Internal Components - **H08 Batch Validator**: For validation logic -- **F17 Database Layer**: For metadata persistence +- **F03 Flight Database**: For metadata persistence +- **F02 Flight Processor**: For flight state information ### External Dependencies - **opencv-python**: Image I/O diff --git a/docs/02_components/06_image_rotation_manager/image_rotation_manager_spec.md b/docs/02_components/06_image_rotation_manager/image_rotation_manager_spec.md index d46b759..a999d40 100644 --- a/docs/02_components/06_image_rotation_manager/image_rotation_manager_spec.md +++ b/docs/02_components/06_image_rotation_manager/image_rotation_manager_spec.md @@ -13,7 +13,7 @@ class IImageRotationManager(ABC): pass @abstractmethod - def try_rotation_steps(self, flight_id: str, image: np.ndarray, satellite_tile: np.ndarray) -> Optional[RotationResult]: + def try_rotation_steps(self, flight_id: str, frame_id: int, image: np.ndarray, satellite_tile: np.ndarray, tile_bounds: TileBounds, timestamp: datetime) -> Optional[RotationResult]: pass @abstractmethod @@ -25,7 +25,7 @@ class IImageRotationManager(ABC): pass @abstractmethod - def update_heading(self, flight_id: str, heading: float) -> bool: + def update_heading(self, flight_id: str, frame_id: int, heading: float, timestamp: datetime) -> bool: pass @abstractmethod @@ -41,29 +41,29 @@ class IImageRotationManager(ABC): pass @abstractmethod - def try_chunk_rotation_steps(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray) -> Optional[RotationResult]: + def try_chunk_rotation_steps(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[RotationResult]: pass ``` ## Component Description ### Responsibilities -- Handle UAV image rotation preprocessing for LiteSAM -- **Critical**: LiteSAM fails if images rotated >45°, requires preprocessing +- Handle UAV image rotation preprocessing +- **Critical**: LiteSAM (F09 Metric Refinement) fails if images rotated >45°, requires preprocessing - Perform 30° step rotation sweeps (12 rotations: 0°, 30°, 60°, ..., 330°) - Track UAV heading angle across flight - Calculate precise rotation angle from homography point correspondences - Detect sharp turns requiring rotation sweep - Pre-rotate images to known heading for subsequent frames - **Chunk rotation operations (rotate all images in chunk)** -- **Chunk rotation sweeps for LiteSAM matching** +- **Chunk rotation sweeps (delegates matching to F09 Metric Refinement)** ### Scope -- Image rotation operations +- Image rotation operations (pure rotation, no matching) - UAV heading tracking and history - Sharp turn detection -- Rotation sweep coordination with LiteSAM matching -- Precise angle calculation from homography +- Rotation sweep coordination (rotates images, delegates matching to F09 Metric Refinement) +- Precise angle calculation from homography (extracted from F09 results) - **Chunk-level rotation (all images rotated by same angle)** ## API Methods @@ -103,9 +103,9 @@ np.ndarray # Rotated image (same dimensions) --- -### `try_rotation_steps(flight_id: str, image: np.ndarray, satellite_tile: np.ndarray) -> Optional[RotationResult]` +### `try_rotation_steps(flight_id: str, frame_id: int, image: np.ndarray, satellite_tile: np.ndarray, tile_bounds: TileBounds, timestamp: datetime) -> Optional[RotationResult]` -**Description**: Performs 30° rotation sweep, trying LiteSAM match for each rotation. +**Description**: Performs 30° rotation sweep, rotating image at each step and delegating matching to F09 Metric Refinement. **Called By**: - Internal (when requires_rotation_sweep() returns True) @@ -114,8 +114,11 @@ np.ndarray # Rotated image (same dimensions) **Input**: ```python flight_id: str +frame_id: int # Frame identifier for heading persistence image: np.ndarray # UAV image satellite_tile: np.ndarray # Satellite reference tile +tile_bounds: TileBounds # GPS bounds and GSD of satellite tile (for F09) +timestamp: datetime # Timestamp for heading persistence ``` **Output**: @@ -132,23 +135,23 @@ RotationResult: ``` For angle in [0°, 30°, 60°, 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300°, 330°]: rotated_image = rotate_image_360(image, angle) - result = LiteSAM.align_to_satellite(rotated_image, satellite_tile) + result = F09.align_to_satellite(rotated_image, satellite_tile, tile_bounds) if result.matched and result.confidence > threshold: precise_angle = calculate_precise_angle(result.homography, angle) - update_heading(flight_id, precise_angle) + update_heading(flight_id, frame_id, precise_angle, timestamp) return RotationResult(matched=True, initial_angle=angle, precise_angle=precise_angle, ...) return None # No match found ``` **Processing Flow**: 1. For each 30° step: - - Rotate image - - Call F09 Metric Refinement (LiteSAM) + - Rotate image via rotate_image_360() + - Call F09 Metric Refinement.align_to_satellite(rotated_image, satellite_tile, tile_bounds) - Check if match found 2. If match found: - - Calculate precise angle from homography - - Update UAV heading - - Return result + - Calculate precise angle from homography via calculate_precise_angle() + - Update UAV heading via update_heading() + - Return RotationResult 3. If no match: - Return None (triggers progressive search expansion) @@ -228,7 +231,7 @@ Optional[float]: Heading angle in degrees (0-360), or None if not initialized --- -### `update_heading(flight_id: str, heading: float) -> bool` +### `update_heading(flight_id: str, frame_id: int, heading: float, timestamp: datetime) -> bool` **Description**: Updates UAV heading angle after successful match. @@ -239,7 +242,9 @@ Optional[float]: Heading angle in degrees (0-360), or None if not initialized **Input**: ```python flight_id: str +frame_id: int # Frame identifier for database persistence heading: float # New heading angle (0-360) +timestamp: datetime # Timestamp for database persistence ``` **Output**: @@ -251,7 +256,7 @@ bool: True if updated successfully 1. Normalize angle to 0-360 range 2. Add to heading history (last 10 headings) 3. Update current_heading for flight -4. Persist to database (optional) +4. **Persist to database**: Call F03 Flight Database.save_heading(flight_id, frame_id, heading, timestamp) **Test Cases**: 1. **Update heading**: Sets new heading @@ -365,17 +370,18 @@ List[np.ndarray] # Rotated images (same dimensions) --- -### `try_chunk_rotation_steps(chunk_images: List[np.ndarray], satellite_tile: np.ndarray) -> Optional[RotationResult]` +### `try_chunk_rotation_steps(chunk_images: List[np.ndarray], satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[RotationResult]` -**Description**: Performs 30° rotation sweep on entire chunk, trying LiteSAM match for each rotation. +**Description**: Performs 30° rotation sweep on entire chunk, rotating all images at each step and delegating matching to F09 Metric Refinement. **Called By**: -- F11 Failure Recovery Coordinator (chunk LiteSAM matching with rotation) +- F11 Failure Recovery Coordinator (chunk matching with rotation) **Input**: ```python chunk_images: List[np.ndarray] # Chunk images satellite_tile: np.ndarray # Reference satellite tile +tile_bounds: TileBounds # GPS bounds and GSD of satellite tile (for F09) ``` **Output**: @@ -392,7 +398,7 @@ RotationResult: ``` For angle in [0°, 30°, 60°, 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300°, 330°]: rotated_chunk = rotate_chunk_360(chunk_images, angle) - result = LiteSAM.align_chunk_to_satellite(rotated_chunk, satellite_tile) + result = F09.align_chunk_to_satellite(rotated_chunk, satellite_tile, tile_bounds) if result.matched and result.confidence > threshold: precise_angle = calculate_precise_angle(result.homography, angle) return RotationResult(matched=True, initial_angle=angle, precise_angle=precise_angle, ...) @@ -401,17 +407,17 @@ return None # No match found **Processing Flow**: 1. For each 30° step: - - Rotate all chunk images - - Call F09 Metric Refinement.align_chunk_to_satellite() + - Rotate all chunk images via rotate_chunk_360() + - Call F09 Metric Refinement.align_chunk_to_satellite(rotated_chunk, satellite_tile, tile_bounds) - Check if match found 2. If match found: - - Calculate precise angle from homography + - Calculate precise angle from homography via calculate_precise_angle() - Return RotationResult 3. If no match: - Return None **Performance**: -- 12 rotations × chunk LiteSAM (~60ms) = ~720ms +- 12 rotations × chunk matching via F09 (~60ms) = ~720ms - Acceptable for chunk matching (async operation) **Test Cases**: @@ -425,18 +431,19 @@ return None # No match found ### Test 1: First Frame Rotation Sweep 1. First frame arrives (no heading set) 2. requires_rotation_sweep() → True -3. try_rotation_steps() → rotates 12 times -4. Match found at 60° step -5. calculate_precise_angle() → 62.3° -6. update_heading(62.3°) -7. Subsequent frames use 62.3° heading +3. try_rotation_steps(flight_id, frame_id=1, image, satellite_tile, tile_bounds, timestamp=now()) → rotates 12 times +4. F09 Metric Refinement called for each rotation +5. Match found at 60° step +6. calculate_precise_angle() → 62.3° +7. update_heading(flight_id, frame_id=1, heading=62.3°, timestamp=now()) +8. Subsequent frames use 62.3° heading ### Test 2: Normal Frame Processing 1. Heading known (90°) 2. requires_rotation_sweep() → False 3. Pre-rotate image to 90° 4. LiteSAM match succeeds with small delta (+2.5°) -5. update_heading(92.5°) +5. update_heading(flight_id, frame_id=237, heading=92.5°, timestamp=now()) ### Test 3: Sharp Turn Detection 1. UAV heading 45° @@ -446,17 +453,19 @@ return None # No match found 5. Perform rotation sweep → find match at 120° step ### Test 4: Tracking Loss Recovery -1. LiteSAM fails to match (no overlap after turn) +1. F09 Metric Refinement fails to match (no overlap after turn) 2. requires_rotation_sweep() → True -3. try_rotation_steps() with all 12 rotations -4. Match found → heading updated +3. try_rotation_steps(flight_id, frame_id, image, satellite_tile, tile_bounds, timestamp) with all 12 rotations +4. F09 called for each rotation step +5. Match found → heading updated ### Test 5: Chunk Rotation Sweeps 1. Build chunk with 10 images (unknown orientation) -2. try_chunk_rotation_steps() with satellite tile -3. Match found at 120° step -4. Precise angle calculated (122.5°) -5. Verify all images rotated consistently +2. try_chunk_rotation_steps(chunk_images, satellite_tile, tile_bounds) with all 12 rotations +3. F09 Metric Refinement called for each rotation +4. Match found at 120° step +5. Precise angle calculated (122.5°) +6. Verify all images rotated consistently ## Non-Functional Requirements @@ -479,9 +488,12 @@ return None # No match found ## Dependencies ### Internal Components -- **F09 Metric Refinement**: For LiteSAM matching during rotation sweep and chunk matching +- **F09 Metric Refinement**: For matching during rotation sweep (align_to_satellite, align_chunk_to_satellite). F06 rotates images, F09 performs the actual matching. - **H07 Image Rotation Utils**: For image rotation and angle calculations - **F12 Route Chunk Manager**: For chunk image retrieval +- **F03 Flight Database**: For heading persistence + +**Note**: `TileBounds` data model is imported from F09 Metric Refinement. ### External Dependencies - **opencv-python**: Image rotation (`cv2.warpAffine`) diff --git a/docs/02_components/08_global_place_recognition/global_place_recognition_spec.md b/docs/02_components/08_global_place_recognition/global_place_recognition_spec.md index 0ccd94e..9e7d291 100644 --- a/docs/02_components/08_global_place_recognition/global_place_recognition_spec.md +++ b/docs/02_components/08_global_place_recognition/global_place_recognition_spec.md @@ -205,18 +205,19 @@ List[TileCandidate] # Re-ranked list ### `initialize_database(satellite_tiles: List[SatelliteTile]) -> bool` -**Description**: Initializes satellite descriptor database during system startup. +**Description**: Loads pre-built satellite descriptor database. **Note**: Semantic index building (DINOv2 descriptors) is performed by the satellite provider service, not during system startup. **Called By**: -- F02 Flight Manager (during system initialization) +- F02 Flight Processor (during system initialization) **Input**: ```python List[SatelliteTile]: tile_id: str - image: np.ndarray + image: np.ndarray # Optional - only if building index locally gps_center: GPSPoint bounds: TileBounds + descriptor: Optional[np.ndarray] # Pre-computed descriptor from provider ``` **Output**: @@ -225,19 +226,31 @@ bool: True if database initialized successfully ``` **Processing Flow**: -1. For each satellite tile: - - compute_location_descriptor(tile.image) → descriptor - - Store descriptor with tile metadata -2. Build Faiss index using H04 Faiss Index Manager -3. Persist index to disk for fast startup +1. **Load pre-built index**: Satellite provider provides pre-computed DINOv2 descriptors +2. If descriptors provided: + - Load descriptors directly + - Build Faiss index using H04 Faiss Index Manager +3. If descriptors not provided (fallback): + - For each satellite tile: + - compute_location_descriptor(tile.image) → descriptor + - Store descriptor with tile metadata + - Build Faiss index +4. Persist index to disk for fast startup + +**Satellite Provider Integration**: +- **Primary**: Satellite provider builds semantic index offline +- Provider exposes index via API or file download +- F08 loads pre-built index at startup +- **Fallback**: F08 can build index locally if provider doesn't supply it **Performance**: -- Initialization time: ~10-30 minutes for 10,000 tiles (one-time cost) -- Can be done offline and loaded at startup +- **Load pre-built index**: <10 seconds (fast startup) +- **Build index locally**: ~10-30 minutes for 10,000 tiles (fallback only) **Test Cases**: -1. **Initialize with 1000 tiles**: Completes successfully -2. **Load pre-built index**: Fast startup (<10s) +1. **Load pre-built index**: Completes successfully, fast startup +2. **Fallback local building**: Builds index if provider doesn't supply it +3. **Index query**: Works correctly after loading --- diff --git a/docs/02_components/10_factor_graph_optimizer/factor_graph_optimizer_spec.md b/docs/02_components/10_factor_graph_optimizer/factor_graph_optimizer_spec.md index 5fc2b9f..eb73d99 100644 --- a/docs/02_components/10_factor_graph_optimizer/factor_graph_optimizer_spec.md +++ b/docs/02_components/10_factor_graph_optimizer/factor_graph_optimizer_spec.md @@ -114,12 +114,21 @@ covariance: np.ndarray # (6, 6) - uncertainty **Scale Resolution**: F07 returns unit translation vectors due to monocular scale ambiguity. F10 resolves scale by: 1. Using altitude prior to constrain Z-axis -2. Computing expected displacement from H02 GSD Calculator: +2. **Computing expected displacement**: Call H02 GSD Calculator.compute_gsd() to get GSD - GSD = (sensor_width × altitude) / (focal_length × resolution_width) - expected_displacement ≈ frame_spacing × GSD (typically ~100m) 3. Scaling: scaled_translation = unit_translation × expected_displacement 4. Global refinement using absolute GPS factors from F09 LiteSAM +**Explicit Flow**: +```python +# In add_relative_factor(): +gsd = H02.compute_gsd(altitude, focal_length, sensor_width, image_width) +expected_displacement = frame_spacing * gsd # ~100m +scaled_translation = relative_pose.translation * expected_displacement +# Add scaled_translation to factor graph +``` + **Output**: ```python bool: True if factor added successfully @@ -689,7 +698,7 @@ OptimizationResult: ### Internal Components - **H03 Robust Kernels**: For Huber/Cauchy loss functions -- **H02 GSD Calculator**: For coordinate conversions +- **H02 GSD Calculator**: For GSD computation and scale resolution ### External Dependencies - **GTSAM**: Graph optimization library diff --git a/docs/02_components/11_failure_recovery_coordinator/failure_recovery_coordinator_spec.md b/docs/02_components/11_failure_recovery_coordinator/failure_recovery_coordinator_spec.md index 4f833e6..6e1ebea 100644 --- a/docs/02_components/11_failure_recovery_coordinator/failure_recovery_coordinator_spec.md +++ b/docs/02_components/11_failure_recovery_coordinator/failure_recovery_coordinator_spec.md @@ -222,7 +222,8 @@ tiles: Dict[str, np.ndarray] # From G04 **Processing Flow**: 1. Get UAV image for frame_id 2. For each tile in grid: - - Call G09.align_to_satellite(uav_image, tile) + - Get tile_bounds via F04.compute_tile_bounds(tile_coords) + - Call F09.align_to_satellite(uav_image, tile, tile_bounds) - If match found with confidence > threshold: - mark_found(session, result) - Return result @@ -457,15 +458,17 @@ Optional[ChunkAlignmentResult]: Match result or None --- -### `merge_chunk_to_trajectory(chunk_id: str, alignment_result: ChunkAlignmentResult) -> bool` +### `merge_chunk_to_trajectory(flight_id: str, chunk_id: str, alignment_result: ChunkAlignmentResult) -> bool` **Description**: Merges chunk into main trajectory after successful matching. **Called By**: - Internal (after chunk LiteSAM matching succeeds) +- process_unanchored_chunks() (which has flight_id from chunk handle) **Input**: ```python +flight_id: str # Flight identifier (available from chunk handle) chunk_id: str alignment_result: ChunkAlignmentResult: chunk_center_gps: GPSPoint @@ -479,13 +482,16 @@ bool: True if merge successful ``` **Processing Flow**: -1. Get chunk anchor frame (middle frame or best frame) -2. Call F12.mark_chunk_anchored() with GPS (F12 coordinates with F10) -3. Find target chunk (previous chunk or main trajectory) -4. Call F12.merge_chunks(chunk_id, target_chunk_id, transform) (F12 coordinates with F10) -5. F12 handles chunk state updates (deactivation, status updates) -6. F10 optimizes merged graph globally (via F12.merge_chunks()) -7. Return True +1. Get chunk frames via F12.get_chunk_frames(chunk_id) → merged_frames (all frames in chunk that will be updated) +2. Get chunk anchor frame (middle frame or best frame) +3. Call F12.mark_chunk_anchored() with GPS (F12 coordinates with F10) +4. Find target chunk (previous chunk or main trajectory) +5. Call F12.merge_chunks(chunk_id, target_chunk_id, transform) (F12 coordinates with F10) +6. F12 handles chunk state updates (deactivation, status updates) +7. F10 optimizes merged graph globally (via F12.merge_chunks()) +8. **Update results**: Call F14 Result Manager.update_results_after_chunk_merge(flight_id, merged_frames) + - F14 retrieves updated poses from F10, converts ENU to GPS, updates database, and publishes via SSE +9. Return True **Sim(3) Transform**: - Translation: GPS offset @@ -493,9 +499,10 @@ bool: True if merge successful - Scale: Resolved from altitude and GSD **Test Cases**: -1. **Merge chunk**: Chunk merged successfully +1. **Merge chunk**: Chunk merged successfully, results updated via F14 2. **Global consistency**: Merged trajectory globally consistent 3. **Multiple chunks**: Can merge multiple chunks sequentially +4. **Result updates**: All merged frames have updated GPS coordinates published --- @@ -525,15 +532,17 @@ while flight_active: if candidates: alignment = try_chunk_litesam_matching(chunk.chunk_id, candidates) if alignment: - merge_chunk_to_trajectory(chunk.chunk_id, alignment) + merge_chunk_to_trajectory(chunk.flight_id, chunk.chunk_id, alignment) sleep(5 seconds) ``` **Background Processing**: +- **Trigger**: Started by F02 Flight Processor after flight creation - Runs asynchronously, doesn't block frame processing -- Periodically checks for ready chunks +- Periodically checks for ready chunks (every 5 seconds) - Attempts matching and merging - Reduces user input requests +- **Lifecycle**: Starts when flight becomes active, stops when flight completed **Test Cases**: 1. **Background matching**: Unanchored chunks matched asynchronously @@ -606,14 +615,15 @@ while flight_active: ## Dependencies ### Internal Components -- F04 Satellite Data Manager (tile grids) +- F04 Satellite Data Manager (tile grids and tile_bounds computation) - F06 Image Rotation Manager (rotation sweep and chunk rotation) - F08 Global Place Recognition (candidates and chunk semantic matching) - F09 Metric Refinement (LiteSAM and chunk LiteSAM matching) - F10 Factor Graph Optimizer (anchor application and chunk merging) -- F02 Flight Manager (status updates) -- F14 SSE Event Streamer (user input events) - F12 Route Chunk Manager (chunk lifecycle) +- F14 Result Manager (result updates after chunk merging) +- F02 Flight Manager (status updates) +- F15 SSE Event Streamer (user input events) ### External Dependencies - None diff --git a/docs/02_components/12_route_chunk_manager/route_chunk_manager_spec.md b/docs/02_components/12_route_chunk_manager/route_chunk_manager_spec.md index 85da672..e64b867 100644 --- a/docs/02_components/12_route_chunk_manager/route_chunk_manager_spec.md +++ b/docs/02_components/12_route_chunk_manager/route_chunk_manager_spec.md @@ -462,13 +462,15 @@ bool: True if merge successful 1. Verify both chunks exist 2. Verify chunk_id_1 is anchored (has_anchor=True) 3. Validate chunks can be merged (not already merged, not same chunk) -4. Call F10.merge_chunks(chunk_id_1, chunk_id_2, transform) -5. Update chunk_id_1 state: +4. **Merge direction**: chunk_id_1 (newer, source) merges INTO chunk_id_2 (older, target) +5. Call F10.merge_chunks(chunk_id_1, chunk_id_2, transform) +6. Update chunk_id_1 state: - Set is_active=False - Set matching_status="merged" - Call deactivate_chunk(chunk_id_1) -6. Update chunk_id_2 state (if needed) -7. Return True +7. Update chunk_id_2 state (if needed) +8. Persist chunk state via F03 Flight Database.save_chunk_state() +9. Return True **Validation**: - Both chunks must exist @@ -476,6 +478,11 @@ bool: True if merge successful - chunk_id_1 must not already be merged - chunk_id_1 and chunk_id_2 must be different +**Merge Direction**: +- **chunk_id_1**: Source chunk (newer, recently anchored) +- **chunk_id_2**: Target chunk (older, main trajectory or previous chunk) +- Newer chunks merge INTO older chunks to maintain chronological consistency + **Test Cases**: 1. **Merge anchored chunks**: Chunks merged successfully, chunk_id_1 deactivated 2. **Merge unanchored chunk**: Returns False (validation fails) @@ -570,6 +577,7 @@ bool: True if marked successfully - **F05 Image Input Pipeline**: Image retrieval - **F08 Global Place Recognition**: Descriptor computation - **F07 Sequential VO**: VO results for chunk building +- **F03 Flight Database**: Chunk state persistence ### External Dependencies - **numpy**: Array operations diff --git a/docs/02_components/13_coordinate_transformer/coordinate_transformer_spec.md b/docs/02_components/13_coordinate_transformer/coordinate_transformer_spec.md index 6a2155d..bdcfbdd 100644 --- a/docs/02_components/13_coordinate_transformer/coordinate_transformer_spec.md +++ b/docs/02_components/13_coordinate_transformer/coordinate_transformer_spec.md @@ -68,26 +68,32 @@ class ICoordinateTransformer(ABC): ### ENU Origin Management -#### `set_enu_origin(origin_gps: GPSPoint) -> None` +#### `set_enu_origin(flight_id: str, origin_gps: GPSPoint) -> None` -**Description**: Sets the ENU (East-North-Up) coordinate system origin. Called once during flight creation using the flight's start_gps. +**Description**: Sets the ENU (East-North-Up) coordinate system origin for a specific flight. Called once during flight creation using the flight's start_gps. + +**Why ENU Origin?**: Factor graph optimization works in Cartesian coordinates (meters) for better numerical stability. ENU converts GPS (degrees) to local meters relative to an origin. See `helpers/enu_origin_explanation.md` for details. **Called By**: - F02 Flight Processor (during create_flight) **Input**: ```python +flight_id: str # Flight identifier origin_gps: GPSPoint - lat: float # Origin latitude in WGS84 - lon: float # Origin longitude in WGS84 + lat: float # Origin latitude in WGS84 (flight start_gps) + lon: float # Origin longitude in WGS84 (flight start_gps) ``` **Processing Flow**: -1. Store origin_gps as ENU origin -2. Precompute conversion factors for lat/lon to meters -3. All subsequent ENU operations use this origin +1. Store origin_gps as ENU origin for flight_id +2. Precompute conversion factors for lat/lon to meters at origin latitude +3. All subsequent ENU operations for this flight use this origin -**Important**: The ENU origin is set to the flight's start_gps during flight creation and remains constant for the entire flight duration. +**Important**: +- Each flight has its own ENU origin (the flight's start_gps) +- Origin remains constant for the entire flight duration +- All frames in the flight use the same origin for ENU conversions **Test Cases**: 1. **Set origin**: Store origin GPS @@ -96,13 +102,18 @@ origin_gps: GPSPoint --- -#### `get_enu_origin() -> GPSPoint` +#### `get_enu_origin(flight_id: str) -> GPSPoint` -**Description**: Returns the currently set ENU origin. +**Description**: Returns the ENU origin for a specific flight. **Called By**: - F10 Factor Graph Optimizer (for coordinate checks) -- F13 Result Manager (for reference) +- F14 Result Manager (for reference) + +**Input**: +```python +flight_id: str +``` **Output**: ```python @@ -110,7 +121,7 @@ GPSPoint: The ENU origin (flight start_gps) ``` **Error Conditions**: -- Raises `OriginNotSetError` if called before set_enu_origin() +- Raises `OriginNotSetError` if called before set_enu_origin() for this flight_id **Test Cases**: 1. **After set_enu_origin**: Returns stored origin @@ -118,9 +129,9 @@ GPSPoint: The ENU origin (flight start_gps) --- -#### `gps_to_enu(gps: GPSPoint) -> Tuple[float, float, float]` +#### `gps_to_enu(flight_id: str, gps: GPSPoint) -> Tuple[float, float, float]` -**Description**: Converts GPS coordinates to ENU (East, North, Up) relative to the set origin. +**Description**: Converts GPS coordinates to ENU (East, North, Up) relative to the flight's ENU origin. **Called By**: - F10 Factor Graph Optimizer (for absolute factors) @@ -128,6 +139,7 @@ GPSPoint: The ENU origin (flight start_gps) **Input**: ```python +flight_id: str # Flight identifier gps: GPSPoint lat: float lon: float @@ -135,7 +147,7 @@ gps: GPSPoint **Output**: ```python -Tuple[float, float, float]: (east, north, up) in meters relative to origin +Tuple[float, float, float]: (east, north, up) in meters relative to flight's origin ``` **Algorithm**: @@ -153,17 +165,18 @@ Tuple[float, float, float]: (east, north, up) in meters relative to origin --- -#### `enu_to_gps(enu: Tuple[float, float, float]) -> GPSPoint` +#### `enu_to_gps(flight_id: str, enu: Tuple[float, float, float]) -> GPSPoint` -**Description**: Converts ENU coordinates back to GPS. +**Description**: Converts ENU coordinates back to GPS using the flight's ENU origin. **Called By**: - F10 Factor Graph Optimizer (for get_trajectory) -- F13 Result Manager (for publishing GPS results) +- F14 Result Manager (for publishing GPS results) **Input**: ```python -enu: Tuple[float, float, float] # (east, north, up) in meters +flight_id: str # Flight identifier +enu: Tuple[float, float, float] # (east, north, up) in meters relative to flight's origin ``` **Output**: @@ -420,7 +433,6 @@ List[Tuple[float, float]]: Transformed points ### Internal Components - **F10 Factor Graph Optimizer**: For frame poses -- **F12 Route Chunk Manager**: For chunk context (chunk-aware coordinate transforms) - **F17 Configuration Manager**: For camera parameters - **H01 Camera Model**: For projection operations - **H02 GSD Calculator**: For GSD calculations diff --git a/docs/02_components/14_result_manager/result_manager_spec.md b/docs/02_components/14_result_manager/result_manager_spec.md index 2f73777..2a6a59e 100644 --- a/docs/02_components/14_result_manager/result_manager_spec.md +++ b/docs/02_components/14_result_manager/result_manager_spec.md @@ -13,7 +13,7 @@ class IResultManager(ABC): pass @abstractmethod - def publish_to_route_api(self, flight_id: str, frame_id: int) -> bool: + def publish_waypoint_update(self, flight_id: str, frame_id: int) -> bool: pass @abstractmethod @@ -34,14 +34,14 @@ class IResultManager(ABC): ### Responsibilities - Manage trajectory results per flight - Track frame refinements and changes -- Trigger per-frame Route API updates via G03 -- Send incremental updates via F14 SSE +- Store waypoint updates via F03 Flight Database +- Send incremental updates via F15 SSE Event Streamer - Maintain result versioning for audit trail - Convert optimized poses to GPS coordinates ### Scope - Result state management -- Route API integration +- Flight Database integration (waypoint storage) - SSE event triggering - Incremental update detection - Result persistence @@ -73,9 +73,9 @@ result: FrameResult: **Output**: `bool` - True if updated **Processing Flow**: -1. Store result in memory/database -2. Call publish_to_route_api() -3. Call G14.send_frame_result() +1. Store result via F03 Flight Database.save_frame_result() +2. Call publish_waypoint_update() +3. Call F15 SSE Event Streamer.send_frame_result() 4. Update flight statistics **Test Cases**: @@ -84,9 +84,9 @@ result: FrameResult: --- -### `publish_to_route_api(flight_id: str, frame_id: int) -> bool` +### `publish_waypoint_update(flight_id: str, frame_id: int) -> bool` -**Description**: Sends frame GPS to Route API via G03 client. +**Description**: Updates waypoint in Flight Database via F03. **Called By**: - Internal (after update_frame_result) @@ -97,17 +97,17 @@ flight_id: str frame_id: int ``` -**Output**: `bool` - True if published successfully +**Output**: `bool` - True if updated successfully **Processing Flow**: 1. Get result for frame_id 2. Convert to Waypoint format -3. Call G03.update_route_waypoint() +3. Call F03 Flight Database.update_waypoint() 4. Handle errors (retry if transient) **Test Cases**: -1. Successful publish → Route API updated -2. Route API unavailable → logs error, continues +1. Successful update → Waypoint stored in database +2. Database unavailable → logs error, continues --- @@ -151,11 +151,11 @@ frame_ids: List[int] # Frames with updated poses **Processing Flow**: 1. For each frame_id: - - Get refined pose from F10 - - Convert to GPS via G12 - - Update result with refined=True - - publish_to_route_api() - - Call G14.send_refinement() + - Get refined pose from F10 Factor Graph Optimizer → ENU pose + - Convert ENU pose to GPS via F13 Coordinate Transformer.enu_to_gps(flight_id, enu_pose) + - Update result with refined=True via F03 Flight Database.save_frame_result() + - Update waypoint via F03 Flight Database.update_waypoint() + - Call F15 SSE Event Streamer.send_refinement() **Test Cases**: 1. Batch refinement → all frames updated and published @@ -167,7 +167,7 @@ frame_ids: List[int] # Frames with updated poses **Description**: Gets frames changed since timestamp (for incremental updates). **Called By**: -- F14 SSE Event Streamer (for reconnection replay) +- F15 SSE Event Streamer (for reconnection replay) **Input**: ```python @@ -181,19 +181,49 @@ since: datetime 1. Get changes → returns only modified frames 2. No changes → returns empty list +--- + +### `update_results_after_chunk_merge(flight_id: str, merged_frames: List[int]) -> bool` + +**Description**: Updates frame results after chunk merging changes frame poses. + +**Called By**: +- F11 Failure Recovery Coordinator (after chunk merging) + +**Input**: +```python +flight_id: str +merged_frames: List[int] # Frames whose poses changed due to chunk merge +``` + +**Output**: `bool` - True if updated successfully + +**Processing Flow**: +1. For each frame_id in merged_frames: + - Get updated pose from F10 Factor Graph Optimizer.get_trajectory() → ENU pose + - Convert ENU pose to GPS via F13 Coordinate Transformer.enu_to_gps(flight_id, enu_pose) + - Update frame result via F03 Flight Database.save_frame_result() + - Update waypoint via F03 Flight Database.update_waypoint() + - Send refinement event via F15 SSE Event Streamer.send_refinement() +2. Return True + +**Test Cases**: +1. **Chunk merge updates**: All merged frames updated and published +2. **GPS accuracy**: Updated GPS matches optimized poses + ## Integration Tests ### Test 1: Per-Frame Processing 1. Process frame 237 2. update_frame_result() → stores result -3. Verify publish_to_route_api() called -4. Verify F14 SSE event sent +3. Verify publish_waypoint_update() called (F03.update_waypoint()) +4. Verify F15 SSE event sent ### Test 2: Batch Refinement 1. Process 100 frames 2. Factor graph refines frames 10-50 3. mark_refined([10-50]) → updates all -4. Verify Route API updated +4. Verify Flight Database updated (F03.batch_update_waypoints()) 5. Verify SSE refinement events sent ### Test 3: Incremental Updates @@ -207,22 +237,22 @@ since: datetime ### Performance - **update_frame_result**: < 50ms -- **publish_to_route_api**: < 100ms (non-blocking) +- **publish_waypoint_update**: < 100ms (non-blocking) - **get_flight_results**: < 200ms for 2000 frames ### Reliability - Result persistence survives crashes -- Guaranteed at-least-once delivery to Route API +- Guaranteed at-least-once delivery to Flight Database - Idempotent updates ## Dependencies ### Internal Components -- G03 Route API Client -- F10 Factor Graph Optimizer -- F12 Coordinate Transformer -- F14 SSE Event Streamer -- F17 Database Layer +- **F03 Flight Database**: For waypoint and frame result persistence +- **F10 Factor Graph Optimizer**: For refined pose retrieval +- **F13 Coordinate Transformer**: For ENU to GPS conversion +- **F15 SSE Event Streamer**: For real-time result streaming +- **F11 Failure Recovery Coordinator**: Triggers chunk merge result updates ### External Dependencies - None diff --git a/docs/02_components/astral_next_components_diagram.drawio b/docs/02_components/astral_next_components_diagram.drawio index d7090b7..864b5a1 100644 --- a/docs/02_components/astral_next_components_diagram.drawio +++ b/docs/02_components/astral_next_components_diagram.drawio @@ -4,8 +4,8 @@ - - + + @@ -37,84 +37,84 @@ - - + + - + - - + + - + - + - + - + - + - - + + - - + + - - + + - - + + - - + + - - + + - - + + + + + - + - + - + - + - + - - - @@ -149,37 +149,40 @@ - + - + - + - + - - + + - - + + - - + + - - + + - - + + - - + + - - + + + + + @@ -199,29 +202,65 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - + + - - + + - - + + @@ -230,16 +269,16 @@ - + - - + + - - + + - - + + @@ -256,21 +295,30 @@ - + - + - + - + - + + + + + + + + + + @@ -282,9 +330,12 @@ - + + + + diff --git a/docs/02_components/chunking_system_assessment.md b/docs/02_components/chunking_system_assessment.md deleted file mode 100644 index 416a29b..0000000 --- a/docs/02_components/chunking_system_assessment.md +++ /dev/null @@ -1,440 +0,0 @@ -# Chunking System Assessment: F12 Route Chunk Manager and Integration - -## Executive Summary - -The chunking system implements the Atlas multi-map architecture from the solution document, with **F12 Route Chunk Manager** serving as the high-level coordinator. The system demonstrates **strong coherence** with clear separation of concerns between F12 (lifecycle), F10 (factor graph), and F11 (matching coordination). However, there are some **integration inconsistencies** and **missing lifecycle transitions** that should be addressed. - -**Overall Assessment: 8/10** - Well-designed with minor gaps - ---- - -## 1. Architecture Coherence - -### 1.1 Separation of Concerns ✅ - -The chunking system properly separates responsibilities: - -| Component | Responsibility | Level | -|-----------|---------------|-------| -| **F12 Route Chunk Manager** | Chunk lifecycle, state tracking, matching coordination | High-level (flight context) | -| **F10 Factor Graph Optimizer** | Chunk subgraphs, factor management, optimization | Low-level (factor graph) | -| **F11 Failure Recovery Coordinator** | Chunk creation triggers, matching orchestration | Coordination layer | -| **F02 Flight Processor** | Chunk-aware frame processing | Application layer | - -**Assessment:** ✅ **Excellent separation** - Clear boundaries, no overlap - -### 1.2 Chunk Lifecycle Completeness - -**Lifecycle States:** -- `unanchored` → `matching` → `anchored` → `merged` - -**Lifecycle Methods:** - -| State Transition | F12 Method | F10 Method | Status | -|------------------|------------|------------|--------| -| **Creation** | `create_chunk()` | `create_new_chunk()` | ✅ Complete | -| **Building** | `add_frame_to_chunk()` | `add_relative_factor_to_chunk()` | ✅ Complete | -| **Matching** | `is_chunk_ready_for_matching()` | N/A | ✅ Complete | -| **Anchoring** | `mark_chunk_anchored()` | `add_chunk_anchor()` | ✅ Complete | -| **Merging** | `deactivate_chunk()` | `merge_chunks()` | ⚠️ **Gap** | -| **Deactivation** | `deactivate_chunk()` | N/A | ✅ Complete | - -**Status:** ✅ **Fixed** - Added `F12.merge_chunks()` method for chunk merging coordination - ---- - -## 2. Integration Analysis - -### 2.1 F12 ↔ F10 Integration - -**F12 calls F10:** -- ✅ `create_chunk()` → `F10.create_new_chunk()` - Correct -- ✅ `add_frame_to_chunk()` → `F10.add_relative_factor_to_chunk()` - Correct -- ✅ `mark_chunk_anchored()` → `F10.add_chunk_anchor()` - Correct -- ✅ `get_chunk_bounds()` → `F10.get_chunk_trajectory()` - Correct - -**F10 exposes chunk operations:** -- ✅ `create_new_chunk()` - Low-level factor graph operation -- ✅ `add_relative_factor_to_chunk()` - Factor management -- ✅ `add_chunk_anchor()` - Anchor management -- ✅ `merge_chunks()` - Sim(3) transformation -- ✅ `optimize_chunk()` - Chunk-level optimization -- ✅ `get_chunk_trajectory()` - Trajectory retrieval - -**Assessment:** ✅ **Well-integrated** - F12 properly wraps F10 operations - -**Status:** ✅ **Fixed** - F10's method renamed to `get_chunk_for_frame(frame_id)` for clarity, F12's `get_active_chunk(flight_id)` remains for high-level queries. - ---- - -### 2.2 F12 ↔ F11 Integration - -**F11 calls F12:** -- ✅ `create_chunk_on_tracking_loss()` → `F12.create_chunk()` - Correct (proactive) -- ✅ `try_chunk_semantic_matching()` → Uses F12 methods indirectly - Correct -- ✅ `try_chunk_litesam_matching()` → Uses F12 methods indirectly - Correct -- ✅ `merge_chunk_to_trajectory()` → Calls `F10.merge_chunks()` directly - ⚠️ **Bypasses F12** - -**F12 provides for F11:** -- ✅ `get_chunks_for_matching()` - Returns unanchored chunks -- ✅ `get_chunk_images()` - Image retrieval -- ✅ `get_chunk_composite_descriptor()` - Descriptor computation -- ✅ `get_chunk_bounds()` - Bounds for tile search - -**Assessment:** ⚠️ **Minor inconsistency** - F11 bypasses F12 for merging - -**Status:** ✅ **Fixed** - F11 now calls `F12.merge_chunks()` which coordinates with F10 and updates chunk states. - ---- - -### 2.3 F12 ↔ F02 Integration - -**F02 calls F12:** -- ✅ `get_active_chunk(flight_id)` - Before processing frame -- ✅ `create_new_chunk(flight_id, frame_id)` - On tracking loss -- ✅ `add_frame_to_chunk()` - During frame processing - -**F02 chunk-aware processing:** -- ✅ Gets active chunk before processing frame -- ✅ Creates new chunk on tracking loss -- ✅ Adds frames to chunk with VO results - -**Assessment:** ✅ **Well-integrated** - F02 properly uses F12 for chunk management - ---- - -### 2.4 F12 ↔ F08/F09 Integration - -**F08 Global Place Recognition:** -- ✅ `get_chunk_images()` - Retrieves chunk images -- ✅ `get_chunk_composite_descriptor()` - Gets aggregate descriptor -- ⚠️ **Issue:** F12's `get_chunk_composite_descriptor()` calls `F08.compute_location_descriptor()` for each image, but F08 also has `compute_chunk_descriptor()` method. This creates duplication. - -**F09 Metric Refinement:** -- ✅ `get_chunk_images()` - Retrieves chunk images -- ✅ `align_chunk_to_satellite()` - Chunk-to-satellite matching - -**Assessment:** ⚠️ **Descriptor duplication** - F12 and F08 both compute chunk descriptors - ---- - -## 3. Chunk Lifecycle Flow Analysis - -### 3.1 Normal Chunk Lifecycle - -``` -1. Tracking Lost (F02/F11 detects) - ↓ -2. create_chunk() (F12) → create_new_chunk() (F10) - ↓ -3. add_frame_to_chunk() × N (F12) → add_relative_factor_to_chunk() (F10) - ↓ -4. is_chunk_ready_for_matching() (F12) → True (>=5 frames) - ↓ -5. get_chunks_for_matching() (F12) → Returns chunk - ↓ -6. try_chunk_semantic_matching() (F11) → Uses F12.get_chunk_composite_descriptor() - ↓ -7. try_chunk_litesam_matching() (F11) → Uses F12.get_chunk_images() - ↓ -8. mark_chunk_anchored() (F12) → add_chunk_anchor() (F10) - ↓ -9. merge_chunks() (F10) → Called directly by F11 (bypasses F12) - ↓ -10. deactivate_chunk() (F12) → Chunk marked as merged -``` - -**Status:** ✅ **Fixed** - Step 9 now goes through F12.merge_chunks() maintaining abstraction - ---- - -### 3.2 Proactive Chunk Creation - -**Solution Requirement:** Chunks created proactively on tracking loss, not reactively after matching failures. - -**Implementation:** -- ✅ F11's `create_chunk_on_tracking_loss()` creates chunk immediately -- ✅ F02's `handle_tracking_loss()` creates chunk proactively -- ✅ Processing continues in new chunk while matching happens asynchronously - -**Assessment:** ✅ **Correctly implements proactive creation** - ---- - -### 3.3 Chunk Matching Strategy - -**Solution Requirement:** Chunk semantic matching (aggregate DINOv2) → Chunk LiteSAM matching (rotation sweeps) → Chunk merging (Sim3) - -**Implementation:** -- ✅ F12 provides `get_chunk_composite_descriptor()` for semantic matching -- ✅ F11 coordinates semantic matching via F08 -- ✅ F11 coordinates LiteSAM matching via F09 with rotation sweeps -- ✅ F10 provides `merge_chunks()` with Sim(3) transform - -**Assessment:** ✅ **Correctly implements matching strategy** - ---- - -## 4. State Management Coherence - -### 4.1 ChunkHandle State Fields - -**F12 ChunkHandle:** -```python -chunk_id: str -flight_id: str -start_frame_id: int -end_frame_id: Optional[int] -frames: List[int] -is_active: bool -has_anchor: bool -anchor_frame_id: Optional[int] -anchor_gps: Optional[GPSPoint] -matching_status: str # "unanchored", "matching", "anchored", "merged" -``` - -**Assessment:** ✅ **Complete state representation** - All necessary fields present - -**Issue:** `matching_status` and `has_anchor` are redundant (if `matching_status == "anchored"`, then `has_anchor == True`). Consider consolidating. - ---- - -### 4.2 State Transitions - -**Valid Transitions:** -- `unanchored` → `matching` (when matching starts) -- `matching` → `anchored` (when anchor found) -- `anchored` → `merged` (when merged to global trajectory) -- `unanchored` → `anchored` (direct anchor, e.g., user input) - -**Assessment:** ✅ **State transitions are well-defined** - -**Status:** ✅ **Fixed** - Added `F12.mark_chunk_matching(chunk_id)` method for explicit state transitions. - ---- - -## 5. Missing Functionality - -### 5.1 Chunk Merging Coordination - -**Gap:** F12 doesn't have a method to coordinate chunk merging. - -**Current:** F11 calls `F10.merge_chunks()` directly, bypassing F12. - -**Recommendation:** Add `F12.merge_chunks(chunk_id_1, chunk_id_2, transform)` that: -1. Validates chunks can be merged -2. Calls `F10.merge_chunks()` -3. Updates chunk states (deactivates chunk_id_1, updates chunk_id_2) -4. Updates `matching_status` to "merged" - ---- - -### 5.2 Chunk State Persistence - -**Gap:** No explicit persistence of chunk state. - -**Current:** Chunk state is in-memory only (via F12 and F10). - -**Recommendation:** F12 should persist chunk state to F03 Flight Database for: -- Recovery after system restart -- Chunk state queries -- Debugging and analysis - ---- - -### 5.3 Chunk Matching Status Updates - -**Gap:** No explicit method to update `matching_status` to "matching". - -**Current:** Status transitions happen implicitly in F11. - -**Recommendation:** Add `F12.mark_chunk_matching(chunk_id)` to explicitly track matching state. - ---- - -## 6. Inconsistencies - -### 6.1 Descriptor Computation Duplication - -**Issue:** Both F08 and F12 compute chunk descriptors. - -- **F08:** `compute_chunk_descriptor(chunk_images)` - Computes aggregate DINOv2 descriptor -- **F12:** `get_chunk_composite_descriptor(chunk_id)` - Also computes aggregate descriptor - -**Current Implementation (F12):** -```python -1. Get chunk images via get_chunk_images() -2. For each image: - - Call F08.compute_location_descriptor(image) → descriptor -3. Aggregate descriptors (mean, max, or VLAD) -``` - -**Status:** ✅ **Fixed** - F12 now calls `F08.compute_chunk_descriptor()` directly, eliminating duplication. - ---- - -### 6.2 Active Chunk Query Inconsistency - -**Issue:** F10 and F12 have different signatures for `get_active_chunk()`. - -- **F10:** `get_active_chunk(frame_id: int)` - Query by frame ID -- **F12:** `get_active_chunk(flight_id: str)` - Query by flight ID - -**Status:** ✅ **Fixed** - F10's method renamed to `get_chunk_for_frame(frame_id)` for clarity, clearly distinguishing from F12's `get_active_chunk(flight_id)`. - ---- - -### 6.3 Chunk Merging Bypass - -**Issue:** F11 bypasses F12 when merging chunks. - -**Current:** `F11.merge_chunk_to_trajectory()` → `F10.merge_chunks()` directly - -**Status:** ✅ **Fixed** - F11 now calls `F12.merge_chunks()` which properly coordinates merging and updates chunk states. - ---- - -## 7. Strengths - -### 7.1 Clear Abstraction Layers ✅ - -- **F12** provides high-level chunk lifecycle management -- **F10** provides low-level factor graph operations -- Clear separation of concerns - -### 7.2 Proactive Chunk Creation ✅ - -- Chunks created immediately on tracking loss -- Processing continues while matching happens asynchronously -- Matches solution requirement perfectly - -### 7.3 Complete Chunk State Tracking ✅ - -- ChunkHandle captures all necessary state -- State transitions are well-defined -- Matching status tracks chunk progress - -### 7.4 Chunk Matching Integration ✅ - -- F12 provides chunk representations (images, descriptors, bounds) -- F11 coordinates matching strategies -- F08/F09 perform actual matching - -### 7.5 Chunk Isolation ✅ - -- Each chunk has independent subgraph in F10 -- Factors isolated to chunks -- Local optimization before global merging - ---- - -## 8. Weaknesses - -### 8.1 Missing Merging Coordination ✅ **FIXED** - -- ✅ F12.merge_chunks() method added -- ✅ F11 now calls F12, maintaining abstraction -- ✅ State updates handled by F12 - -### 8.2 Descriptor Computation Duplication ✅ **FIXED** - -- ✅ F12 now delegates to F08.compute_chunk_descriptor() directly -- ✅ Single source of truth for chunk descriptor computation - -### 8.3 No State Persistence ⚠️ - -- Chunk state is in-memory only -- No recovery after restart -- No debugging/analysis capabilities -- **Note:** This is a Priority 2 enhancement, not critical - -### 8.4 Implicit State Transitions ✅ **FIXED** - -- ✅ F12.mark_chunk_matching() method added -- ✅ Explicit state transitions for matching status -- ✅ Easier to track chunk state changes - ---- - -## 9. Recommendations - -### Priority 1: Critical Fixes ✅ **COMPLETED** - -1. ✅ **Add F12.merge_chunks() method** - **FIXED** - - F12.merge_chunks() method added - - Coordinates chunk merging and updates states - - F11 now calls F12, maintaining abstraction - -2. ✅ **Fix descriptor computation duplication** - **FIXED** - - F12.get_chunk_composite_descriptor() now calls F08.compute_chunk_descriptor() directly - - Removed duplicate aggregation logic from F12 - -3. ✅ **Add explicit matching status updates** - **FIXED** - - F12.mark_chunk_matching(chunk_id) method added - - Explicitly tracks when matching starts - -### Priority 2: Important Enhancements - -4. **Add chunk state persistence** - - Persist ChunkHandle to F03 Flight Database - - Enable recovery after restart - - Support debugging and analysis - -5. ✅ **Clarify method naming** - **FIXED** - - F10.get_active_chunk() renamed to get_chunk_for_frame() for clarity - - Documented different use cases (low-level vs high-level) - -6. **Add chunk validation** - - Validate chunk state before operations - - Prevent invalid state transitions - - Better error messages - -### Priority 3: Nice-to-Have - -7. **Add chunk metrics** - - Track chunk creation time - - Track matching success rate - - Track merging statistics - -8. **Add chunk query methods** - - Query chunks by status - - Query chunks by frame range - - Query chunks by matching status - ---- - -## 10. Overall Assessment - -### Coherence Score: 9/10 (Updated after fixes) - -**Strengths:** -- ✅ Clear separation of concerns -- ✅ Proactive chunk creation -- ✅ Complete lifecycle coverage -- ✅ Well-integrated with F10, F11, F02 -- ✅ Proper chunk isolation -- ✅ **FIXED:** Merging coordination through F12 -- ✅ **FIXED:** Descriptor computation delegation -- ✅ **FIXED:** Explicit state transitions - -**Remaining Enhancement:** -- ⚠️ No state persistence (Priority 2, not critical) - -**Conclusion:** The chunking system is **excellent and coherent** with clear architectural boundaries. All Priority 1 issues have been **fixed**. The system now properly maintains abstraction layers with F12 coordinating all chunk lifecycle operations. Remaining enhancement (state persistence) is a nice-to-have feature for recovery and debugging. - ---- - -## 11. Solution Alignment - -### Atlas Multi-Map Architecture ✅ - -The chunking system correctly implements the Atlas architecture from the solution: - -- ✅ **Chunks are first-class entities** - F12 manages chunks as primary units -- ✅ **Proactive chunk creation** - Chunks created immediately on tracking loss -- ✅ **Independent chunk processing** - Each chunk has its own subgraph in F10 -- ✅ **Chunk matching and merging** - Semantic matching → LiteSAM → Sim(3) merging -- ✅ **Multiple chunks simultaneously** - System supports multiple unanchored chunks - -**Assessment:** ✅ **Fully aligned with solution architecture** - diff --git a/docs/02_components/component_coverage_analysis.md b/docs/02_components/component_coverage_analysis.md deleted file mode 100644 index e7e3467..0000000 --- a/docs/02_components/component_coverage_analysis.md +++ /dev/null @@ -1,526 +0,0 @@ -# Component Coverage Analysis: Solution, Problem, Acceptance Criteria, and Restrictions - -## Executive Summary - -This document analyzes how the ASTRAL-Next component architecture covers the solution design, addresses the original problem, meets acceptance criteria, and operates within restrictions. - -**Key Findings:** -- ✅ Components comprehensively implement the tri-layer localization strategy (Sequential VO, Global PR, Metric Refinement) -- ✅ Atlas multi-map chunk architecture properly handles sharp turns and disconnected routes -- ✅ All 10 acceptance criteria are addressed by specific component capabilities -- ✅ Restrictions are respected through component design choices -- ⚠️ Some architectural concerns identified (see architecture_assessment.md) - ---- - -## 1. Solution Coverage Analysis - -### 1.1 Tri-Layer Localization Strategy - -The solution document specifies three layers operating concurrently: - -| Solution Layer | Component(s) | Implementation Status | -|----------------|--------------|----------------------| -| **L1: Sequential Tracking** | F07 Sequential Visual Odometry | ✅ Fully covered | -| **L2: Global Re-Localization** | F08 Global Place Recognition | ✅ Fully covered | -| **L3: Metric Refinement** | F09 Metric Refinement | ✅ Fully covered | -| **State Estimation** | F10 Factor Graph Optimizer | ✅ Fully covered | - -**Coverage Details:** - -**L1 - Sequential VO (F07):** -- Uses SuperPoint + LightGlue as specified -- Handles <5% overlap scenarios -- Provides relative pose factors to F10 -- Chunk-aware operations (factors added to chunk subgraphs) - -**L2 - Global PR (F08):** -- Implements AnyLoc (DINOv2 + VLAD) as specified -- Faiss indexing for efficient retrieval -- Chunk semantic matching (aggregate descriptors) -- Handles "kidnapped robot" scenarios - -**L3 - Metric Refinement (F09):** -- Implements LiteSAM for cross-view matching -- Requires pre-rotation (handled by F06) -- Extracts GPS from homography -- Chunk-to-satellite matching support - -**State Estimation (F10):** -- GTSAM-based factor graph optimization -- Robust kernels (Huber/Cauchy) for outlier handling -- Multi-chunk support (Atlas architecture) -- Sim(3) transformation for chunk merging - -### 1.2 Atlas Multi-Map Architecture - -**Solution Requirement:** Chunks are first-class entities, created proactively on tracking loss. - -**Component Coverage:** -- ✅ **F12 Route Chunk Manager**: Manages chunk lifecycle (creation, activation, matching, merging) -- ✅ **F10 Factor Graph Optimizer**: Provides multi-chunk factor graph with independent subgraphs -- ✅ **F11 Failure Recovery Coordinator**: Proactively creates chunks on tracking loss -- ✅ **F02 Flight Processor**: Chunk-aware frame processing - -**Chunk Lifecycle Flow:** -1. **Tracking Loss Detected** → F11 creates chunk proactively (not reactive) -2. **Chunk Building** → F07 adds VO factors to chunk subgraph via F10 -3. **Chunk Matching** → F08 (semantic) + F09 (LiteSAM) match chunks -4. **Chunk Anchoring** → F10 anchors chunk with GPS -5. **Chunk Merging** → F10 merges chunks using Sim(3) transform - -**Coverage Verification:** -- ✅ Chunks created proactively (not after matching failures) -- ✅ Chunks processed independently -- ✅ Chunk semantic matching (aggregate DINOv2) -- ✅ Chunk LiteSAM matching with rotation sweeps -- ✅ Chunk merging via Sim(3) transformation - -### 1.3 REST API + SSE Architecture - -**Solution Requirement:** Background service with REST API and SSE streaming. - -**Component Coverage:** -- ✅ **F01 Flight API**: REST endpoints (FastAPI) -- ✅ **F15 SSE Event Streamer**: Real-time result streaming -- ✅ **F02 Flight Processor**: Background processing orchestration -- ✅ **F14 Result Manager**: Result publishing coordination - -**API Coverage:** -- ✅ `POST /flights` - Flight creation -- ✅ `GET /flights/{id}` - Flight retrieval -- ✅ `POST /flights/{id}/images/batch` - Batch image upload -- ✅ `POST /flights/{id}/user-fix` - User anchor input -- ✅ `GET /flights/{id}/stream` - SSE streaming - -**SSE Events:** -- ✅ `frame_processed` - Per-frame results -- ✅ `frame_refined` - Refinement updates -- ✅ `user_input_needed` - User intervention required -- ✅ `search_expanded` - Progressive search updates - -### 1.4 Human-in-the-Loop Strategy - -**Solution Requirement:** User input for 20% of route where automation fails. - -**Component Coverage:** -- ✅ **F11 Failure Recovery Coordinator**: Monitors confidence, triggers user input -- ✅ **F01 Flight API**: Accepts user fixes via REST endpoint -- ✅ **F15 SSE Event Streamer**: Sends user input requests -- ✅ **F10 Factor Graph Optimizer**: Applies user anchors as hard constraints - -**Recovery Stages:** -1. ✅ Stage 1: Progressive tile search (single-image) -2. ✅ Stage 2: Chunk building and semantic matching -3. ✅ Stage 3: Chunk LiteSAM matching with rotation sweeps -4. ✅ Stage 4: User input (last resort) - ---- - -## 2. Original Problem Coverage - -### 2.1 Problem Statement - -**Original Problem:** Determine GPS coordinates of image centers from UAV flight, given only starting GPS coordinates. - -**Component Coverage:** -- ✅ **F13 Coordinate Transformer**: Converts pixel coordinates to GPS -- ✅ **F09 Metric Refinement**: Extracts GPS from satellite alignment -- ✅ **F10 Factor Graph Optimizer**: Optimizes trajectory to GPS coordinates -- ✅ **F14 Result Manager**: Publishes GPS results per frame - -**Coverage Verification:** -- ✅ Starting GPS used to initialize ENU coordinate system (F13) -- ✅ Per-frame GPS computed from trajectory (F10 → F13) -- ✅ Object coordinates computed via pixel-to-GPS transformation (F13) - -### 2.2 Image Processing Requirements - -**Requirement:** Process images taken consecutively within 100m spacing. - -**Component Coverage:** -- ✅ **F05 Image Input Pipeline**: Handles sequential image batches -- ✅ **F07 Sequential VO**: Processes consecutive frames -- ✅ **F02 Flight Processor**: Validates sequence continuity - -**Coverage Verification:** -- ✅ Batch validation ensures sequential ordering -- ✅ VO handles 100m spacing via relative pose estimation -- ✅ Factor graph maintains trajectory continuity - -### 2.3 Satellite Data Usage - -**Requirement:** Use external satellite provider for ground checks. - -**Component Coverage:** -- ✅ **F04 Satellite Data Manager**: Fetches Google Maps tiles -- ✅ **F08 Global Place Recognition**: Matches UAV images to satellite tiles -- ✅ **F09 Metric Refinement**: Aligns UAV images to satellite tiles - -**Coverage Verification:** -- ✅ Google Maps Static API integration (F04) -- ✅ Tile caching and prefetching (F04) -- ✅ Progressive tile search (1→4→9→16→25) (F04 + F11) - ---- - -## 3. Acceptance Criteria Coverage - -### AC-1: 80% of photos < 50m error - -**Component Coverage:** -- **F09 Metric Refinement**: LiteSAM achieves ~17.86m RMSE (within 50m requirement) -- **F10 Factor Graph Optimizer**: Fuses measurements for accuracy -- **F13 Coordinate Transformer**: Accurate GPS conversion - -**Implementation:** -- LiteSAM provides pixel-level alignment -- Factor graph optimization reduces drift -- Altitude priors resolve scale ambiguity - -**Status:** ✅ Covered - ---- - -### AC-2: 60% of photos < 20m error - -**Component Coverage:** -- **F09 Metric Refinement**: LiteSAM RMSE ~17.86m (close to 20m requirement) -- **F10 Factor Graph Optimizer**: Global optimization improves precision -- **F04 Satellite Data Manager**: High-resolution tiles (Zoom Level 19, ~0.30m/pixel) - -**Implementation:** -- Multi-scale LiteSAM processing -- Per-keyframe scale model in factor graph -- High-resolution satellite tiles - -**Status:** ✅ Covered (may require Tier-2 commercial data per solution doc) - ---- - -### AC-3: Robust to 350m outlier - -**Component Coverage:** -- **F10 Factor Graph Optimizer**: Robust kernels (Huber/Cauchy) downweight outliers -- **F11 Failure Recovery Coordinator**: Detects outliers and triggers recovery -- **F07 Sequential VO**: Reports low confidence for outlier frames - -**Implementation:** -- Huber loss function in factor graph -- M-estimation automatically rejects high-residual constraints -- Stage 2 failure logic discards outlier frames - -**Status:** ✅ Covered - ---- - -### AC-4: Robust to sharp turns (<5% overlap) - -**Component Coverage:** -- **F12 Route Chunk Manager**: Creates new chunks on tracking loss -- **F08 Global Place Recognition**: Re-localizes after sharp turns -- **F06 Image Rotation Manager**: Handles unknown orientation -- **F11 Failure Recovery Coordinator**: Coordinates recovery - -**Implementation:** -- Proactive chunk creation on tracking loss -- Rotation sweeps (0°, 30°, ..., 330°) for unknown orientation -- Chunk semantic matching handles featureless terrain -- Chunk LiteSAM matching aggregates correspondences - -**Status:** ✅ Covered - ---- - -### AC-5: < 10% outlier anchors - -**Component Coverage:** -- **F10 Factor Graph Optimizer**: Robust M-estimation (Huber loss) -- **F09 Metric Refinement**: Match confidence filtering -- **F11 Failure Recovery Coordinator**: Validates matches before anchoring - -**Implementation:** -- Huber loss automatically downweights bad anchors -- Match confidence threshold (0.7) filters outliers -- Inlier count validation before anchoring - -**Status:** ✅ Covered - ---- - -### AC-6: Connect route chunks; User input - -**Component Coverage:** -- **F12 Route Chunk Manager**: Manages chunk lifecycle -- **F10 Factor Graph Optimizer**: Merges chunks via Sim(3) transform -- **F11 Failure Recovery Coordinator**: Coordinates chunk matching -- **F01 Flight API**: User input endpoint -- **F15 SSE Event Streamer**: User input requests - -**Implementation:** -- Chunk semantic matching connects chunks -- Chunk LiteSAM matching provides Sim(3) transform -- Chunk merging maintains global consistency -- User input as last resort (Stage 4) - -**Status:** ✅ Covered - ---- - -### AC-7: < 5 seconds processing/image - -**Component Coverage:** -- **F16 Model Manager**: TensorRT optimization (2-4x speedup) -- **F07 Sequential VO**: ~50ms (SuperPoint + LightGlue) -- **F08 Global Place Recognition**: ~150ms (DINOv2 + VLAD, keyframes only) -- **F09 Metric Refinement**: ~60ms (LiteSAM) -- **F10 Factor Graph Optimizer**: ~100ms (iSAM2 incremental) - -**Performance Breakdown:** -- Sequential VO: ~50ms -- Global PR (keyframes): ~150ms -- Metric Refinement: ~60ms -- Factor Graph: ~100ms -- **Total (worst case): ~360ms << 5s** - -**Status:** ✅ Covered (with TensorRT optimization) - ---- - -### AC-8: Real-time stream + async refinement - -**Component Coverage:** -- **F15 SSE Event Streamer**: Real-time frame results -- **F14 Result Manager**: Per-frame and refinement publishing -- **F10 Factor Graph Optimizer**: Asynchronous batch refinement -- **F02 Flight Processor**: Decoupled processing pipeline - -**Implementation:** -- Immediate per-frame results via SSE -- Background refinement thread -- Batch waypoint updates for refinements -- Incremental SSE events for refinements - -**Status:** ✅ Covered - ---- - -### AC-9: Image Registration Rate > 95% - -**Component Coverage:** -- **F07 Sequential VO**: Handles <5% overlap -- **F12 Route Chunk Manager**: Chunk creation prevents "lost" frames -- **F08 Global Place Recognition**: Re-localizes after tracking loss -- **F09 Metric Refinement**: Aligns frames to satellite - -**Implementation:** -- "Lost track" creates new chunk (not registration failure) -- Chunk matching recovers disconnected segments -- System never "fails" - fragments and continues - -**Status:** ✅ Covered (Atlas architecture ensures >95%) - ---- - -### AC-10: Mean Reprojection Error (MRE) < 1.0px - -**Component Coverage:** -- **F10 Factor Graph Optimizer**: Local and global bundle adjustment -- **F07 Sequential VO**: High-quality feature matching (SuperPoint + LightGlue) -- **F09 Metric Refinement**: Precise homography estimation - -**Implementation:** -- Local BA in sequential VO -- Global BA in factor graph optimizer -- Per-keyframe scale model minimizes graph tension -- Robust kernels prevent outlier contamination - -**Status:** ✅ Covered - ---- - -## 4. Restrictions Compliance - -### R-1: Photos from airplane-type UAVs only - -**Component Coverage:** -- **F17 Configuration Manager**: Validates flight type -- **F02 Flight Processor**: Validates flight parameters - -**Compliance:** ✅ Validated at flight creation - ---- - -### R-2: Camera pointing downwards, fixed, not autostabilized - -**Component Coverage:** -- **F06 Image Rotation Manager**: Handles rotation variations -- **F09 Metric Refinement**: Requires pre-rotation (handled by F06) -- **F07 Sequential VO**: Handles perspective variations - -**Compliance:** ✅ Rotation sweeps handle fixed camera orientation - ---- - -### R-3: Flying range restricted to Eastern/Southern Ukraine - -**Component Coverage:** -- **F02 Flight Processor**: Validates waypoints within operational area -- **F04 Satellite Data Manager**: Prefetches tiles for operational area -- **F13 Coordinate Transformer**: ENU origin set to operational area - -**Compliance:** ✅ Geofence validation, operational area constraints - ---- - -### R-4: Image resolution FullHD to 6252×4168 - -**Component Coverage:** -- **F16 Model Manager**: TensorRT handles variable resolutions -- **F07 Sequential VO**: SuperPoint processes variable resolutions -- **F05 Image Input Pipeline**: Validates image dimensions - -**Compliance:** ✅ Components handle variable resolutions - ---- - -### R-5: Altitude predefined, no more than 1km - -**Component Coverage:** -- **F10 Factor Graph Optimizer**: Altitude priors resolve scale -- **F13 Coordinate Transformer**: GSD calculations use altitude -- **F02 Flight Processor**: Validates altitude <= 1000m - -**Compliance:** ✅ Altitude used as soft constraint in factor graph - ---- - -### R-6: NO data from IMU - -**Component Coverage:** -- **F10 Factor Graph Optimizer**: Monocular VO only (no IMU factors) -- **F07 Sequential VO**: Pure visual odometry -- **F13 Coordinate Transformer**: Scale resolved via altitude + satellite matching - -**Compliance:** ✅ No IMU components, scale resolved via altitude priors - ---- - -### R-7: Flights mostly in sunny weather - -**Component Coverage:** -- **F08 Global Place Recognition**: DINOv2 handles appearance changes -- **F09 Metric Refinement**: LiteSAM robust to lighting variations -- **F07 Sequential VO**: SuperPoint handles texture variations - -**Compliance:** ✅ Algorithms robust to lighting conditions - ---- - -### R-8: Google Maps (may be outdated) - -**Component Coverage:** -- **F04 Satellite Data Manager**: Google Maps Static API integration -- **F08 Global Place Recognition**: DINOv2 semantic features (invariant to temporal changes) -- **F09 Metric Refinement**: LiteSAM focuses on structural features - -**Compliance:** ✅ Semantic matching handles outdated satellite data - ---- - -### R-9: 500-1500 photos typically, up to 3000 - -**Component Coverage:** -- **F05 Image Input Pipeline**: Batch processing (10-50 images) -- **F10 Factor Graph Optimizer**: Efficient optimization (iSAM2) -- **F03 Flight Database**: Handles large flight datasets - -**Compliance:** ✅ Components scale to 3000 images - ---- - -### R-10: Sharp turns possible (exception, not rule) - -**Component Coverage:** -- **F12 Route Chunk Manager**: Chunk architecture handles sharp turns -- **F11 Failure Recovery Coordinator**: Recovery strategies for sharp turns -- **F06 Image Rotation Manager**: Rotation sweeps for unknown orientation - -**Compliance:** ✅ Chunk architecture handles exceptions gracefully - ---- - -### R-11: Processing on RTX 2060/3070 (TensorRT required) - -**Component Coverage:** -- **F16 Model Manager**: TensorRT optimization (FP16 quantization) -- **F07 Sequential VO**: TensorRT-optimized SuperPoint + LightGlue -- **F08 Global Place Recognition**: TensorRT-optimized DINOv2 -- **F09 Metric Refinement**: TensorRT-optimized LiteSAM - -**Compliance:** ✅ All models optimized for TensorRT, FP16 quantization - ---- - -## 5. Coverage Gaps and Concerns - -### 5.1 Architectural Concerns - -See `architecture_assessment.md` for detailed concerns: -- Component numbering inconsistencies -- Circular dependencies (F14 → F01) -- Duplicate functionality (chunk descriptors) -- Missing component connections - -### 5.2 Potential Gaps - -1. **Performance Monitoring**: H05 Performance Monitor exists but integration unclear -2. **Error Recovery**: Comprehensive error handling not fully specified -3. **Concurrent Flights**: Multi-flight processing not fully validated -4. **Satellite Data Freshness**: Handling of outdated Google Maps data relies on semantic features (may need validation) - -### 5.3 Recommendations - -1. **Fix Architectural Issues**: Address concerns in architecture_assessment.md -2. **Performance Validation**: Validate <5s processing on RTX 2060 -3. **Accuracy Validation**: Test against ground truth data (coordinates.csv) -4. **Chunk Matching Validation**: Validate chunk matching reduces user input by 50-70% - ---- - -## 6. Summary Matrix - -| Requirement Category | Coverage | Status | -|---------------------|----------|--------| -| **Solution Architecture** | Tri-layer + Atlas + REST/SSE | ✅ Complete | -| **Problem Statement** | GPS localization from images | ✅ Complete | -| **AC-1** (80% < 50m) | LiteSAM + Factor Graph | ✅ Covered | -| **AC-2** (60% < 20m) | LiteSAM + High-res tiles | ✅ Covered | -| **AC-3** (350m outlier) | Robust kernels | ✅ Covered | -| **AC-4** (Sharp turns) | Chunk architecture | ✅ Covered | -| **AC-5** (<10% outliers) | Robust M-estimation | ✅ Covered | -| **AC-6** (Chunk connection) | Chunk matching + User input | ✅ Covered | -| **AC-7** (<5s processing) | TensorRT optimization | ✅ Covered | -| **AC-8** (Real-time stream) | SSE + Async refinement | ✅ Covered | -| **AC-9** (>95% registration) | Atlas architecture | ✅ Covered | -| **AC-10** (MRE < 1.0px) | Bundle adjustment | ✅ Covered | -| **Restrictions** | All 11 restrictions | ✅ Compliant | - ---- - -## 7. Conclusion - -The component architecture comprehensively covers the solution design, addresses the original problem, meets all acceptance criteria, and operates within restrictions. The Atlas multi-map chunk architecture is properly implemented across F10, F11, and F12 components. The tri-layer localization strategy is fully covered by F07, F08, and F09. - -**Key Strengths:** -- Complete solution coverage -- All acceptance criteria addressed -- Restrictions respected -- Chunk architecture properly implemented - -**Areas for Improvement:** -- Fix architectural concerns (see architecture_assessment.md) -- Validate performance on target hardware -- Test accuracy against ground truth data -- Validate chunk matching effectiveness - diff --git a/docs/_metodology/tutorial.md b/docs/_metodology/tutorial.md index 4447993..9acac7e 100644 --- a/docs/_metodology/tutorial.md +++ b/docs/_metodology/tutorial.md @@ -85,42 +85,50 @@ # 2. Planning phase -## 2.1 **🤖📋AI plan**: Generate components +## 2.10 **🤖📋AI plan**: Generate components - ### Execute `/gen_components` + ### Execute `/2.10_gen_components` ### Revise - Revise the plan, answer questions, put detailed descriptions - Make sure stored components are coherent and make sense - - Ask AI - ``` - analyze carefully interaction between components. All covered? also, state that each component should implement interface, so that they could be interchangeable with different implementations - ``` ### Store plan save plan to `docs/02_components/00_decomposition_plan.md` -## 2.2 **🤖📋AI plan**: Generate tests - - ### Execute `/gen_tests` +## 2.15 **🤖📋AI plan**: Components assesment + + ### Execute `/2.15_components_assesment` ### Revise - - Revise the tests, answer questions, put detailed descriptions - - Make sure stored tests are coherent and make sense - -## 2.3 **🤖📋AI plan**: Generate Jira Epics + - Revise the plan, answer questions, put detailed descriptions + - Make sure stored components are coherent and make sense - ### Execute `/gen_epics` + ### plan + save plan to `docs/02_components/00_decomposition_plan.md` + +## 2.20 **🤖📋AI plan**: Generate Jira Epics + + ### Execute `/2.20/_gen_epics` ### Revise - Revise the epics, answer questions, put detailed descriptions - Make sure epics are coherent and make sense -## 2.4 **🤖📋AI plan**: Component Decomposition To Features + +## 2.30 **🤖📋AI plan**: Generate tests + + ### Execute `/2.30_gen_tests` + + ### Revise + - Revise the tests, answer questions, put detailed descriptions + - Make sure stored tests are coherent and make sense + +## 2.40 **🤖📋AI plan**: Component Decomposition To Features ### Execute For each component in `docs/02_components` run - `/gen_features --component @docs/02_components/[##]_[component_name]/[component_name]_spec.md` + `/2.40_gen_features --component @docs/02_components/[##]_[component_name]/[component_name]_spec.md` ### Revise - Revise the features, answer questions, put detailed descriptions