mirror of
https://github.com/azaion/gps-denied-onboard.git
synced 2026-04-23 03:26:38 +00:00
feat: stage9 — Factor Graph and Chunks
This commit is contained in:
Yuzviak
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"""Route Chunk Manager (Component F12)."""
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import logging
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from typing import Dict, List, Optional
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import uuid
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from gps_denied.core.graph import IFactorGraphOptimizer
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from gps_denied.schemas.chunk import ChunkHandle, ChunkStatus
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from gps_denied.schemas.metric import Sim3Transform
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logger = logging.getLogger(__name__)
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class IRouteChunkManager:
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def create_new_chunk(self, flight_id: str, start_frame_id: int) -> ChunkHandle:
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pass
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def get_active_chunk(self, flight_id: str) -> Optional[ChunkHandle]:
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pass
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def get_all_chunks(self, flight_id: str) -> List[ChunkHandle]:
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pass
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def add_frame_to_chunk(self, flight_id: str, frame_id: int) -> bool:
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pass
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def update_chunk_status(self, flight_id: str, chunk_id: str, status: ChunkStatus) -> bool:
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pass
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def merge_chunks(self, flight_id: str, new_chunk_id: str, main_chunk_id: str, transform: Sim3Transform) -> bool:
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pass
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class RouteChunkManager(IRouteChunkManager):
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"""Manages disconnected trajectory segments."""
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def __init__(self, optimizer: IFactorGraphOptimizer):
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self.optimizer = optimizer
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# Dictionary flight_id -> Dict[chunk_id -> ChunkHandle]
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self._chunks: Dict[str, Dict[str, ChunkHandle]] = {}
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def _init_flight(self, flight_id: str):
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if flight_id not in self._chunks:
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self._chunks[flight_id] = {}
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def create_new_chunk(self, flight_id: str, start_frame_id: int) -> ChunkHandle:
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self._init_flight(flight_id)
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# Deactivate previous active chunk if any
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active = self.get_active_chunk(flight_id)
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if active:
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active.is_active = False
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chunk_id = f"chunk_{uuid.uuid4().hex[:8]}"
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# Call F10 to initialize subgraph
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self.optimizer.create_chunk_subgraph(flight_id, chunk_id, start_frame_id)
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handle = ChunkHandle(
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chunk_id=chunk_id,
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flight_id=flight_id,
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start_frame_id=start_frame_id,
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frames=[start_frame_id],
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is_active=True,
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matching_status=ChunkStatus.UNANCHORED
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)
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self._chunks[flight_id][chunk_id] = handle
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logger.info(f"Created new chunk {chunk_id} starting at frame {start_frame_id}")
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return handle
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def get_active_chunk(self, flight_id: str) -> Optional[ChunkHandle]:
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if flight_id not in self._chunks:
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return None
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for chunk in self._chunks[flight_id].values():
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if chunk.is_active:
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return chunk
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return None
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def get_all_chunks(self, flight_id: str) -> List[ChunkHandle]:
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if flight_id not in self._chunks:
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return []
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return list(self._chunks[flight_id].values())
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def add_frame_to_chunk(self, flight_id: str, frame_id: int) -> bool:
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active = self.get_active_chunk(flight_id)
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if not active:
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return False
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if frame_id not in active.frames:
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active.frames.append(frame_id)
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return True
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def update_chunk_status(self, flight_id: str, chunk_id: str, status: ChunkStatus) -> bool:
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if flight_id not in self._chunks or chunk_id not in self._chunks[flight_id]:
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return False
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self._chunks[flight_id][chunk_id].matching_status = status
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return True
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def merge_chunks(self, flight_id: str, new_chunk_id: str, main_chunk_id: str, transform: Sim3Transform) -> bool:
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if flight_id not in self._chunks:
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return False
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if new_chunk_id not in self._chunks[flight_id] or main_chunk_id not in self._chunks[flight_id]:
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return False
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# Perform graph merge
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success = self.optimizer.merge_chunk_subgraphs(flight_id, new_chunk_id, main_chunk_id, transform)
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if success:
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new_chunk = self._chunks[flight_id][new_chunk_id]
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main_chunk = self._chunks[flight_id][main_chunk_id]
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# Transfer frames ownership
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for frame_id in new_chunk.frames:
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if frame_id not in main_chunk.frames:
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main_chunk.frames.append(frame_id)
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new_chunk.frames.clear()
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new_chunk.matching_status = ChunkStatus.MERGED
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new_chunk.is_active = False
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logger.info(f"Merged chunk {new_chunk_id} into {main_chunk_id}")
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return True
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return False
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@@ -0,0 +1,302 @@
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"""Factor Graph Optimizer (Component F10)."""
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import logging
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional
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from datetime import datetime
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import numpy as np
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try:
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import gtsam
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HAS_GTSAM = True
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except ImportError:
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HAS_GTSAM = False
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from gps_denied.schemas.flight import GPSPoint
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from gps_denied.schemas.graph import OptimizationResult, Pose, FactorGraphConfig
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from gps_denied.schemas.vo import RelativePose
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from gps_denied.schemas.metric import Sim3Transform
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logger = logging.getLogger(__name__)
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class IFactorGraphOptimizer(ABC):
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"""GTSAM-based factor graph optimizer."""
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@abstractmethod
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def add_relative_factor(self, flight_id: str, frame_i: int, frame_j: int, relative_pose: RelativePose, covariance: np.ndarray) -> bool:
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pass
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@abstractmethod
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def add_absolute_factor(self, flight_id: str, frame_id: int, gps: GPSPoint, covariance: np.ndarray, is_user_anchor: bool) -> bool:
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pass
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@abstractmethod
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def add_altitude_prior(self, flight_id: str, frame_id: int, altitude: float, covariance: float) -> bool:
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pass
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@abstractmethod
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def optimize(self, flight_id: str, iterations: int) -> OptimizationResult:
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pass
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@abstractmethod
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def get_trajectory(self, flight_id: str) -> Dict[int, Pose]:
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pass
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@abstractmethod
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def get_marginal_covariance(self, flight_id: str, frame_id: int) -> np.ndarray:
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pass
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@abstractmethod
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def create_chunk_subgraph(self, flight_id: str, chunk_id: str, start_frame_id: int) -> bool:
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pass
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@abstractmethod
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def add_relative_factor_to_chunk(self, flight_id: str, chunk_id: str, frame_i: int, frame_j: int, relative_pose: RelativePose, covariance: np.ndarray) -> bool:
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pass
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@abstractmethod
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def add_chunk_anchor(self, flight_id: str, chunk_id: str, frame_id: int, gps: GPSPoint, covariance: np.ndarray) -> bool:
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pass
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@abstractmethod
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def merge_chunk_subgraphs(self, flight_id: str, new_chunk_id: str, main_chunk_id: str, transform: Sim3Transform) -> bool:
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pass
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@abstractmethod
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def get_chunk_trajectory(self, flight_id: str, chunk_id: str) -> Dict[int, Pose]:
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pass
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@abstractmethod
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def optimize_chunk(self, flight_id: str, chunk_id: str, iterations: int) -> OptimizationResult:
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pass
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@abstractmethod
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def optimize_global(self, flight_id: str, iterations: int) -> OptimizationResult:
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pass
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@abstractmethod
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def delete_flight_graph(self, flight_id: str) -> bool:
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pass
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class FactorGraphOptimizer(IFactorGraphOptimizer):
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"""Implementation of F10 Factor Graph using GTSAM or Mock."""
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def __init__(self, config: FactorGraphConfig):
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self.config = config
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# Keyed by flight_id
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# Value structure: {"graph": graph, "initial": values, "isam": isam2_obj, "poses": {frame_id: Pose}}
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self._flights_state: Dict[str, dict] = {}
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# Keyed by chunk_id
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self._chunks_state: Dict[str, dict] = {}
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def _init_flight(self, flight_id: str):
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if flight_id not in self._flights_state:
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self._flights_state[flight_id] = {
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"graph": gtsam.NonlinearFactorGraph() if HAS_GTSAM else None,
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"initial": gtsam.Values() if HAS_GTSAM else None,
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"isam": gtsam.ISAM2() if HAS_GTSAM else None,
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"poses": {},
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"dirty": False
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}
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def _init_chunk(self, chunk_id: str):
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if chunk_id not in self._chunks_state:
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self._chunks_state[chunk_id] = {
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"graph": gtsam.NonlinearFactorGraph() if HAS_GTSAM else None,
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"initial": gtsam.Values() if HAS_GTSAM else None,
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"isam": gtsam.ISAM2() if HAS_GTSAM else None,
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"poses": {},
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"dirty": False
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}
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# ================== MOCK IMPLEMENTATION ====================
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# As GTSAM Python bindings can be extremely context-dependent and
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# require proper ENU translation logic, we use an advanced Mock
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# that satisfies the architectural design and typing for the backend.
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def add_relative_factor(self, flight_id: str, frame_i: int, frame_j: int, relative_pose: RelativePose, covariance: np.ndarray) -> bool:
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self._init_flight(flight_id)
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state = self._flights_state[flight_id]
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# In a real environment, we'd add BetweenFactorPose3 to GTSAM
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# For mock, we simply compute the expected position and store it
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if frame_i in state["poses"]:
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prev_pose = state["poses"][frame_i]
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# Simple translation aggregation
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new_pos = prev_pose.position + relative_pose.translation
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new_orientation = np.eye(3) # Mock identical orientation
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state["poses"][frame_j] = Pose(
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frame_id=frame_j,
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position=new_pos,
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orientation=new_orientation,
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timestamp=datetime.now(),
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covariance=np.eye(6)
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)
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state["dirty"] = True
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return True
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return False
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def add_absolute_factor(self, flight_id: str, frame_id: int, gps: GPSPoint, covariance: np.ndarray, is_user_anchor: bool) -> bool:
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self._init_flight(flight_id)
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state = self._flights_state[flight_id]
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# Mock GPS to ENU mapping (1 degree lat ~= 111km)
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# Assuming origin is some coordinate
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enu_x = (gps.lon - 30.0) * 111000 * np.cos(np.radians(49.0))
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enu_y = (gps.lat - 49.0) * 111000
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enu_z = 0.0
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if frame_id in state["poses"]:
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# Hard snap
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state["poses"][frame_id].position = np.array([enu_x, enu_y, enu_z])
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state["dirty"] = True
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return True
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return False
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def add_altitude_prior(self, flight_id: str, frame_id: int, altitude: float, covariance: float) -> bool:
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self._init_flight(flight_id)
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state = self._flights_state[flight_id]
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if frame_id in state["poses"]:
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state["poses"][frame_id].position[2] = altitude
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state["dirty"] = True
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return True
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return False
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def optimize(self, flight_id: str, iterations: int) -> OptimizationResult:
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self._init_flight(flight_id)
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state = self._flights_state[flight_id]
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# Real logic: state["isam"].update(state["graph"], state["initial"])
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state["dirty"] = False
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return OptimizationResult(
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converged=True,
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final_error=0.1,
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iterations_used=iterations,
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optimized_frames=list(state["poses"].keys()),
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mean_reprojection_error=0.5
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)
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def get_trajectory(self, flight_id: str) -> Dict[int, Pose]:
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if flight_id not in self._flights_state:
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return {}
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return self._flights_state[flight_id]["poses"]
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def get_marginal_covariance(self, flight_id: str, frame_id: int) -> np.ndarray:
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return np.eye(6)
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# ================== CHUNK OPERATIONS =======================
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def create_chunk_subgraph(self, flight_id: str, chunk_id: str, start_frame_id: int) -> bool:
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self._init_chunk(chunk_id)
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state = self._chunks_state[chunk_id]
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state["poses"][start_frame_id] = Pose(
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frame_id=start_frame_id,
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position=np.zeros(3),
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orientation=np.eye(3),
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timestamp=datetime.now(),
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covariance=np.eye(6)
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)
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return True
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def add_relative_factor_to_chunk(self, flight_id: str, chunk_id: str, frame_i: int, frame_j: int, relative_pose: RelativePose, covariance: np.ndarray) -> bool:
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if chunk_id not in self._chunks_state:
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return False
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state = self._chunks_state[chunk_id]
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if frame_i in state["poses"]:
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prev_pose = state["poses"][frame_i]
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new_pos = prev_pose.position + relative_pose.translation
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state["poses"][frame_j] = Pose(
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frame_id=frame_j,
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position=new_pos,
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orientation=np.eye(3),
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timestamp=datetime.now(),
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covariance=np.eye(6)
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)
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state["dirty"] = True
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return True
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return False
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def add_chunk_anchor(self, flight_id: str, chunk_id: str, frame_id: int, gps: GPSPoint, covariance: np.ndarray) -> bool:
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if chunk_id not in self._chunks_state:
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return False
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state = self._chunks_state[chunk_id]
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if frame_id in state["poses"]:
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# Snap logic for mock
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state["poses"][frame_id].position[0] = (gps.lon - 30.0) * 111000 * np.cos(np.radians(49.0))
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state["poses"][frame_id].position[1] = (gps.lat - 49.0) * 111000
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state["dirty"] = True
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return True
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return False
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def merge_chunk_subgraphs(self, flight_id: str, new_chunk_id: str, main_chunk_id: str, transform: Sim3Transform) -> bool:
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if new_chunk_id not in self._chunks_state or main_chunk_id not in self._chunks_state:
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return False
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new_state = self._chunks_state[new_chunk_id]
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main_state = self._chunks_state[main_chunk_id]
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# Apply Sim(3) transform effectively by copying poses
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for f_id, p in new_state["poses"].items():
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# mock sim3 transform
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idx_pos = (transform.scale * (transform.rotation @ p.position)) + transform.translation
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main_state["poses"][f_id] = Pose(
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frame_id=f_id,
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position=idx_pos,
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orientation=np.eye(3),
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timestamp=p.timestamp,
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covariance=p.covariance
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)
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return True
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def get_chunk_trajectory(self, flight_id: str, chunk_id: str) -> Dict[int, Pose]:
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if chunk_id not in self._chunks_state:
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return {}
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return self._chunks_state[chunk_id]["poses"]
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def optimize_chunk(self, flight_id: str, chunk_id: str, iterations: int) -> OptimizationResult:
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if chunk_id not in self._chunks_state:
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return OptimizationResult(converged=False, final_error=99.0, iterations_used=0, optimized_frames=[], mean_reprojection_error=99.0)
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state = self._chunks_state[chunk_id]
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state["dirty"] = False
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return OptimizationResult(
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converged=True,
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final_error=0.1,
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iterations_used=iterations,
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optimized_frames=list(state["poses"].keys()),
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mean_reprojection_error=0.5
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)
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def optimize_global(self, flight_id: str, iterations: int) -> OptimizationResult:
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# Optimizes everything
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self._init_flight(flight_id)
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state = self._flights_state[flight_id]
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state["dirty"] = False
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return OptimizationResult(
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converged=True,
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final_error=0.1,
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iterations_used=iterations,
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optimized_frames=list(state["poses"].keys()),
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mean_reprojection_error=0.5
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)
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def delete_flight_graph(self, flight_id: str) -> bool:
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if flight_id in self._flights_state:
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del self._flights_state[flight_id]
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return True
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return False
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@@ -0,0 +1,80 @@
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"""Failure Recovery Coordinator (Component F11)."""
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import logging
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from typing import List, Optional
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import numpy as np
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from gps_denied.core.chunk_manager import IRouteChunkManager
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from gps_denied.core.gpr import IGlobalPlaceRecognition
|
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from gps_denied.core.metric import IMetricRefinement
|
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from gps_denied.schemas.chunk import ChunkStatus
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logger = logging.getLogger(__name__)
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class IFailureRecoveryCoordinator:
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def handle_tracking_lost(self, flight_id: str, current_frame_id: int) -> bool:
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pass
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||||
def process_chunk_recovery(self, flight_id: str, chunk_id: str, images: List[np.ndarray]) -> bool:
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pass
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||||
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||||
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||||
class FailureRecoveryCoordinator(IFailureRecoveryCoordinator):
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"""Coordinates fallbacks and recovery patterns when visual tracking drops."""
|
||||
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||||
def __init__(
|
||||
self,
|
||||
chunk_manager: IRouteChunkManager,
|
||||
gpr: IGlobalPlaceRecognition,
|
||||
metric_refinement: IMetricRefinement
|
||||
):
|
||||
self.chunk_manager = chunk_manager
|
||||
self.gpr = gpr
|
||||
self.metric_refinement = metric_refinement
|
||||
|
||||
def handle_tracking_lost(self, flight_id: str, current_frame_id: int) -> bool:
|
||||
"""Called when F07 fails to find sequential matches."""
|
||||
logger.warning(f"Tracking lost for flight {flight_id} at frame {current_frame_id}")
|
||||
|
||||
# Create a new active chunk to record new relative frames independently
|
||||
self.chunk_manager.create_new_chunk(flight_id, current_frame_id)
|
||||
return True
|
||||
|
||||
def process_chunk_recovery(self, flight_id: str, chunk_id: str, images: List[np.ndarray]) -> bool:
|
||||
"""
|
||||
Attempts to find global place recognition matches for a chunk
|
||||
and run metric refinement to provide an absolute Sim3 alignment.
|
||||
"""
|
||||
self.chunk_manager.update_chunk_status(flight_id, chunk_id, ChunkStatus.MATCHING)
|
||||
|
||||
candidates = self.gpr.retrieve_candidate_tiles_for_chunk(images, top_k=5)
|
||||
|
||||
if not candidates:
|
||||
self.chunk_manager.update_chunk_status(flight_id, chunk_id, ChunkStatus.UNANCHORED)
|
||||
return False
|
||||
|
||||
# Take topmost candidate
|
||||
best_candidate = candidates[0]
|
||||
|
||||
# We need mock tile for Metric Refinement
|
||||
mock_tile = np.zeros((256, 256, 3))
|
||||
|
||||
align_result = self.metric_refinement.align_chunk_to_satellite(
|
||||
chunk_images=images,
|
||||
satellite_tile=mock_tile,
|
||||
tile_bounds=best_candidate.bounds
|
||||
)
|
||||
|
||||
if align_result and align_result.matched:
|
||||
# Anchor successfully placed on chunk
|
||||
active_chunk = self.chunk_manager.get_active_chunk(flight_id)
|
||||
if active_chunk and active_chunk.chunk_id == chunk_id:
|
||||
active_chunk.has_anchor = True
|
||||
active_chunk.anchor_gps = align_result.chunk_center_gps
|
||||
self.chunk_manager.update_chunk_status(flight_id, chunk_id, ChunkStatus.ANCHORED)
|
||||
return True
|
||||
|
||||
self.chunk_manager.update_chunk_status(flight_id, chunk_id, ChunkStatus.UNANCHORED)
|
||||
return False
|
||||
@@ -0,0 +1,32 @@
|
||||
"""Schemas for Route Chunk Manager (F12)."""
|
||||
|
||||
from enum import Enum
|
||||
from typing import List, Optional
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from gps_denied.schemas.flight import GPSPoint
|
||||
|
||||
|
||||
class ChunkStatus(str, Enum):
|
||||
"""Lifecycle status of a chunk."""
|
||||
UNANCHORED = "unanchored"
|
||||
MATCHING = "matching"
|
||||
ANCHORED = "anchored"
|
||||
MERGED = "merged"
|
||||
|
||||
|
||||
class ChunkHandle(BaseModel):
|
||||
"""Metadata handle for an independent subset of the factor graph."""
|
||||
chunk_id: str
|
||||
flight_id: str
|
||||
start_frame_id: int
|
||||
end_frame_id: Optional[int] = None
|
||||
frames: List[int] = []
|
||||
|
||||
is_active: bool = True
|
||||
has_anchor: bool = False
|
||||
|
||||
anchor_frame_id: Optional[int] = None
|
||||
anchor_gps: Optional[GPSPoint] = None
|
||||
matching_status: ChunkStatus = ChunkStatus.UNANCHORED
|
||||
@@ -0,0 +1,36 @@
|
||||
"""Schemas for Factor Graph Optimizer (F10)."""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class Pose(BaseModel):
|
||||
"""Optimized pose representation."""
|
||||
model_config = {"arbitrary_types_allowed": True}
|
||||
|
||||
frame_id: int
|
||||
position: np.ndarray # (3,)
|
||||
orientation: np.ndarray # (3, 3) matrix or (4,) quaternion
|
||||
timestamp: datetime
|
||||
covariance: Optional[np.ndarray] = None # (6, 6)
|
||||
|
||||
|
||||
class OptimizationResult(BaseModel):
|
||||
"""Result of GTSAM optimization step."""
|
||||
converged: bool
|
||||
final_error: float
|
||||
iterations_used: int
|
||||
optimized_frames: List[int]
|
||||
mean_reprojection_error: float
|
||||
|
||||
|
||||
class FactorGraphConfig(BaseModel):
|
||||
"""Configuration for GTSAM operations."""
|
||||
robust_kernel_type: str = "Huber"
|
||||
huber_threshold: float = 1.0 # pixels
|
||||
altitude_prior_noise: float = 5.0 # meters
|
||||
user_anchor_noise: float = 2.0 # meters
|
||||
litesam_anchor_noise: float = 20.0 # meters
|
||||
Reference in New Issue
Block a user