Model uncertainties and dynamic objects are the main challenges for LiDAR-based state estimation in perception systems for autonomous mobile robots/vehicles navigation in unstructured environments. A novel state observer, which integrates point cloud matching updates with a motion model through an optimal variance filter, is designed and experimentally verified in various scenes in urban settings and unstructured environments. The designed state observer addresses the inaccuracies found in environments with poor visibility, such as those with heavy vegetation and dynamic objects, and feature-less scenarios which are challenging for existing LiDAR-based pose estimation methods in autonomous navigation. The detectability of the proposed dynamical process which guarantees the stability of the estimation error dynamics is also studied. We have evaluated the state estimator's performance, in terms of estimation accuracy and processing time in complex urban navigation tasks, and compared it with existing approaches and baselines.
LiDAR-Based Navigation Using Normal Distributions Transform Filter
24.09.2024
2555688 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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