Name
Abstract | ||
|---|---|---|
In this paper, we introduce a modified Generalized Iterative Closest Point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2976132 | IEEE ACCESS |
Keywords | DocType | Volume |
Iterative closest point algorithm,Robustness,Principal component analysis,Covariance matrices,Noise measurement,Licenses,Mirrors,Registration,modified GICP,trimmed method | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Wang | 1 | 0 | 0.34 |
| Yun Li | 2 | 0 | 0.34 |
| Yaxin Peng | 3 | 73 | 16.82 |
| Shihui Ying | 4 | 233 | 23.32 |