Name
Abstract | ||
|---|---|---|
Scan registration plays a critical role in odometry, mapping and localization for Autonomous Ground Vehicle. In this paper, we propose to adopt a probabilistic framework to model the locally planar patch distributions of candidate points from two or more consecutive scans instead of the original point-to-point mode. This can be regarded as the plain-to-plain measurement metric which ensures a very high confidence in the normal orientation of aligned patches. We take into account the geometric attribution of the scanning beam to pick out feature points and then which can reduce the number of selected points to a lower level. The optimization of transform is achieved by the combination of high frequency but coarse scan-to-scan motion estimation and low frequency but fine scan-to-map batch adjustment. We validate the effectiveness of our method by the qualitative tests on our collected point clouds and the quantitative comparisons on the public KITTI odometry datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICInfA.2015.7279468 | 2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION |
Keywords | Field | DocType |
scan registration, plain-to-plain, geometric attribution, batch adjustment | Computer vision,Computer science,Odometry,Feature extraction,Lidar,Planar,Artificial intelligence,Motion estimation,Probabilistic logic,Point cloud,Trajectory | Conference |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chongyang Wei | 1 | 3 | 1.42 |
| Tao Wu | 2 | 58 | 11.53 |
| Hao Fu | 3 | 10 | 2.51 |