Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.
Unstructured with a Point: Validation and Robustness Evaluation of Point-Cloud Based Path Planning
Sae Int. J. Adv. and Curr. Prac. in Mobility
SAE WCX Digital Summit ; 2021
Sae International Journal of Advances and Current Practices in Mobility ; 3 , 4 ; 1776-1789
2021-04-06
14 pages
Aufsatz (Konferenz)
Englisch
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