Name
Abstract | ||
|---|---|---|
LiDAR sensors have been widely applied in autonomous robotics and autonomous systems. High-channel LiDARs or multiple low-channel LiDARs are adopted in these applications to overcome the poor vertical resolution of point clouds, as this scenario can lead to high costs. Here, as a means to improve the vertical resolution of point clouds and lower the cost, we present a 3-D dense rangefinder sensor composed of a low-channel LiDAR, a camera, a brush-less motor, and a crank-link system to replace the traditional LiDAR. A special registration method is designed to register the high-dynamic point cloud. The measurement uncertainty of this method is analyzed. In addition, a 3-D object detection method is used to obtain the 3-D pose of obstacles by combining the dense point cloud and an image-based 2-D object detection algorithm. Finally, several experiments are performed to evaluate the effectiveness of the proposed 3-D rangefinder sensor. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIM.2020.3016415 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
3-D perception sensors, LiDAR, obstacle detection, point cloud, sensor calibration, sensor fusion | Journal | 70 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
27 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Cao | 1 | 0 | 0.34 |
| Pengpeng Su | 2 | 0 | 0.34 |
| Haoyao Chen | 3 | 189 | 23.79 |
| Shiyu Tang | 4 | 0 | 0.34 |
| Liu YH | 5 | 1540 | 185.05 |