Shape Estimation of Negative Obstacles for Autonomous Navigation
Viswadeep Lebakula, Bo Tang, Christopher Goodin, Cindy L. Bethel
Abstract
Obstacle detection and avoidance plays a crucial role in autonomous navigation of unmanned ground vehicles. This becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., holes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road avoiding negative obstacles all the time is not possible. In such cases, the path planner may need to choose a candidate path with a negative obstacle that causes the least amount of damage to the vehicle. To deal better with these types of scenarios, this study introduces a novel approach to perform shape estimation of negative obstacles using LiDAR 3D point cloud data. The dimensions (width, diameter, and depth) and the location (center) of negative obstacles are calculated based on estimated shape. This approach is tested on different terrain types using the Mississippi Autonomous Vehicle Simulation (MAVS).
BibTeX
@inproceedings{iros2021_shapeestimationo,
title = {Shape Estimation of Negative Obstacles for Autonomous Navigation},
author = {Viswadeep Lebakula and Bo Tang and Christopher Goodin and Cindy L. Bethel},
booktitle = {IROS 2021},
year = {2021}
}