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基于距离图像的稀疏 3D 激光扫描仪在非道路环境中的可行驶区域检测与跟踪

Traversable Region Detection and Tracking for a Sparse 3D Laser Scanner for Off-Road Environments Using Range Images.

机构信息

School of Computing, Gachon University, Seongnam-si 1332, Gyeonggi-do, Republic of Korea.

出版信息

Sensors (Basel). 2023 Jun 25;23(13):5898. doi: 10.3390/s23135898.

DOI:10.3390/s23135898
PMID:37447744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10346757/
Abstract

This study proposes a method for detecting and tracking traversable regions in off-road conditions for unmanned ground vehicles (UGVs). Off-road conditions, such as rough terrain or fields, present significant challenges for UGV navigation, and detecting and tracking traversable regions is essential to ensure safe and efficient operation. Using a 3D laser scanner and range-image-based approach, a method is proposed for detecting traversable regions under off-road conditions; this is followed by a Bayesian fusion algorithm for tracking the traversable regions in consecutive frames. Our range-image-based traversable-region-detection approach enables efficient processing of point cloud data from a 3D laser scanner, allowing the identification of traversable areas that are safe for the unmanned ground vehicle to drive on. The effectiveness of the proposed method was demonstrated using real-world data collected during UGV operations on rough terrain, highlighting its potential as a solution for improving UGV navigation capabilities in challenging environments.

摘要

本研究提出了一种用于检测和跟踪无人驾驶地面车辆（UGV）在越野条件下可行驶区域的方法。越野条件，如崎岖地形或田野，对 UGV 导航提出了重大挑战，检测和跟踪可行驶区域对于确保安全和高效运行至关重要。本研究使用 3D 激光扫描仪和基于距离图像的方法，提出了一种在越野条件下检测可行驶区域的方法；然后是一种贝叶斯融合算法，用于在连续帧中跟踪可行驶区域。我们基于距离图像的可行驶区域检测方法能够有效地处理来自 3D 激光扫描仪的点云数据，从而能够识别出无人驾驶地面车辆可以安全行驶的可行驶区域。使用在崎岖地形上进行 UGV 操作时收集的真实世界数据验证了所提出方法的有效性，突出了其在改善 UGV 在挑战性环境中的导航能力方面的潜在应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/8347da29b5c1/sensors-23-05898-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/fb111cc61021/sensors-23-05898-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/34ae07fd5d87/sensors-23-05898-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/8b463261c292/sensors-23-05898-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/ee840b5836e9/sensors-23-05898-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/378762e9ebd3/sensors-23-05898-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/2ce1676067d2/sensors-23-05898-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/fefbf0e1e6d2/sensors-23-05898-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/f0d5b11b4c0f/sensors-23-05898-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/7c4d529ba812/sensors-23-05898-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/5b8699294c3b/sensors-23-05898-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/8347da29b5c1/sensors-23-05898-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/fb111cc61021/sensors-23-05898-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/34ae07fd5d87/sensors-23-05898-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/8b463261c292/sensors-23-05898-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/ee840b5836e9/sensors-23-05898-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/378762e9ebd3/sensors-23-05898-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/2ce1676067d2/sensors-23-05898-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/fefbf0e1e6d2/sensors-23-05898-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/f0d5b11b4c0f/sensors-23-05898-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/7c4d529ba812/sensors-23-05898-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/5b8699294c3b/sensors-23-05898-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b278/10346757/8347da29b5c1/sensors-23-05898-g011.jpg

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