Robust Negative Obstacle Detection in Off-Road Environments Using Multiple LiDARs
文献类型：会议论文
| 作者 | Zhong ZY(钟泽宇)1,2; Wang ZL(王智灵)1 ; Lin LL(林玲龙)1; Liang HW(梁华为)1; Xu FY(徐凤煜)1,2
|
| 出版日期 | 2020-06-04 |
| 会议日期 | 2020-4-20 |
| 关键词 | obstacle detection autonomous vehicle point cloud geometric feature extraction muti-frame fusion |
| 英文摘要 | Autonomous vehicles must analyze and understand the
surrounding terrain in real time when driving in off-road
environments. Yet few UGVs can effectively detect pits and ditches
in an off-road environment at high speeds autonomously. This
paper presents an adaptive negative obstacle detection method for
autonomous vehicles using multiple side-mounted LiDARs. The
method begins by extracting range jump in radial direction from
the raw sensor data to get potential negative obstacle feature point
pairs, and then estimates the vector of the scanned ground surface
around the potential negative obstacles each moment.
Subsequently, the feature point pairs are fifiltered based on several
geometrical features related to the range and the deviation
between the feature point pairs and the ground vector. Then we
fuse the feature point pairs from multiple LiDARs and multiple
frames to improve robustness and the longest detection distance.
A series of experiments have been conducted in several typical off
road scenarios. The experimental results show that the proposed
method can accurately detect negative obstacles and could deal
with complex terrain in off-road environments. |
| 会议录 | 2020 6th International Conference on Control, Automation and Robotics (ICCAR)
 |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/125913]  |
| 专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
| 作者单位 | 1.中科学院合肥物质科学研究院 2.中国科学技术大学 |
| 推荐引用方式 GB/T 7714 | Zhong ZY,Wang ZL,Lin LL,et al. Robust Negative Obstacle Detection in Off-Road Environments Using Multiple LiDARs[C]. 见:. 2020-4-20. |
入库方式： OAI收割
来源：合肥物质科学研究院
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