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An Improved Point Cloud Registration Method Based on Iterative Closest Point algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yu-Jian, Wang Ming-Ming, Wu Tengfe, Lian Qian, Gao |
| Copyright Year | 2018 |
| Abstract | An improved Iterative Closest Point algorithm is reported for the shortage of 3D point cloud registration. The search speed is improved by building KD-tree index on the basis of Iterative Closest Point algorithm. The sampling points are determined by the 3D voxel grid algorithm. The corresponding points of the point cloud are weighted by the normal vector. The point pairs whose weight is larger than the given threshold eliminated. The in uence of the noise points is reduced in use of the method mentioned above. Finally, the outlier data are eliminated by introducing M-estimation to the objective function. The experimental results show that the proposed method can shorten the time of registration e ectively and achieve high registration accuracy. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://actatechnica.com/62(2017)-LQ3/085%20Paper%20Wang%20Yujian.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
