Name
Abstract | ||
|---|---|---|
Detection of the navigable regions for the unmanned surface vehicles (USVs) sailing on the narrow rivers is very important. Existing detection methods mostly depend on the cameras, which is sensitive to environments and cannot provide reliable navigable regions for sailing. In this paper, we propose a scheme to process 3D LiDAR data to achieve an accurate and robust navigable regions detection. We conduct field experiments in a narrow river in different scenarios to prove the performance of the proposed scheme, which reaches on average 93.8% precision and 92.7% recall. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8967860 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Keywords | Field | DocType |
water surface extraction, deep learning, navigable region detection USVs | Computer vision,Computer science,Lidar,Artificial intelligence,Lidar data,Deep learning,Region detection | Conference |
ISSN | Citations | PageRank |
2153-0858 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangtong Yao | 1 | 1 | 0.69 |
| yunxiao shan | 2 | 10 | 3.23 |
| Jieling Li | 3 | 0 | 0.68 |
| Donghui Ma | 4 | 0 | 0.34 |
| Kai Huang | 5 | 468 | 45.69 |