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https://github.com/azaion/gps-denied-onboard.git
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feat: stage9 — Factor Graph and Chunks
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Yuzviak
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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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