Name
Abstract | ||
|---|---|---|
Precise segmentation of road areas using cheap Lidar is a tough and critical task due to data sparsity problem. With sparse point clouds, reliable perception of environment is difficult due to the lack of available information and loss of object features. This paper presents a new approach to use sparse 3D Lidar data for road segmentation by fusing multiple frames of point cloud. With registration of multiple frames into a same coordinate system, reliable data can be provided for later ground segmentation and edge extraction. The accuracies of extensive experiments on three kinds of roads demonstrate that the proposed approach obtains high precision and reliability. |
Year | Venue | Field |
|---|---|---|
2018 | Intelligent Vehicles Symposium | Coordinate system,Computer vision,Edge extraction,Computer science,Segmentation,Feature extraction,Lidar,Artificial intelligence,Lidar data,Point cloud |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfeng Gu | 1 | 0 | 0.34 |
| Yuehui Wang | 2 | 0 | 0.34 |
| Long Chen | 3 | 202 | 31.03 |
| Zhihao Zhao | 4 | 1 | 2.05 |
| Zhe XuanYuan | 5 | 0 | 1.01 |
| Kai Huang | 6 | 468 | 45.69 |