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Unstructured with a Point: Validation and Robustness Evaluation of Point-Cloud Based Path Planning
- Sam Kysar - Michigan Technological University ,
- Jeremy Bos - Michigan Technological University ,
- Akhil Kurup - Michigan Technological University ,
- Zach Jeffries - Michigan Technological University ,
- Jake Carter - Michigan Technological University ,
- Casey Majhor - Michigan Technological University ,
- Paramsothy Jayakumar - US Army ,
- William Smith - US Army GVSC
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Event: SAE WCX Digital Summit
Citation: Kysar, S., Bos, J., Kurup, A., Jeffries, Z. et al., "Unstructured with a Point: Validation and Robustness Evaluation of Point-Cloud Based Path Planning," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(4):1776-1789, 2021, https://doi.org/10.4271/2021-01-0251.
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.