Abstract
Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.
| Original language | English |
|---|---|
| Article number | 3426 |
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Applied Sciences |
| Volume | 11 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 12 Apr 2021 |
Bibliographical note
Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- 3D shape context (3DSC)
- Effective root mean square error (ERMSE)
- Intrinsic shape signatures (ISS)
- Iterative closest point (ICP)
- point clouds registration
- Random sample consensus (RANSAC)