Name
Abstract | ||
|---|---|---|
Using information about objects of surround environment is a good way not only for detecting obstacles but also for estimation of relative translation. Hight-speed lidars provide a lot of information for performing this tasks. But matching one scan to another in some cases lead to different problems. In this paper we describe the solution for building occupancy maps, aligning one scan with another using only information from lidar and processing all data in the real-time using GPU. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MECO.2016.7525736 | 2016 5th Mediterranean Conference on Embedded Computing (MECO) |
Keywords | DocType | ISBN |
autonomous ground vehicles,occupancy map,point cloud,iterative closest points algorithm,LiDAR | Conference | 978-1-5090-2223-6 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanislav A. Goll | 1 | 0 | 1.35 |
| Sergey S. Luksha | 2 | 0 | 1.01 |
| Vladimir S. Leushkin | 3 | 0 | 1.01 |
| Alexandr G. Borisov | 4 | 0 | 1.01 |