Pose estimation with state or measurement constraints is frequently used in autonomous vehicle navigation. With a goal of incorporating constraints inherently, this chapter proposes a dynamic potential field (DPF)‐based formulation to represent states, measurements, and constraints on connected Riemannian manifolds. The state equation and output equation are derived in DPF forms, implying probabilistic inference with states and measurements. Constraints are incorporated by projecting points toward a constraint subset in the state space and measurement space, where the DPF representing constraints is created. An information fusion scheme has been designed for DPFs obtained from multisensor measurements and constraints. KITTI and self‐collected sequences have been used in experiments, during which it is observed that the rotational drift is corrected and translational errors are reduced, thanks to the fusion of stereo visual odometry (SVO), heading measurements, and road maps.
Multi‐Sensor Geometric Pose Estimation
2022-09-27
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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