Abstract
In this paper, a method is proposed for solving relative translations of 3D point clouds collected by Mobile Laser Scanning (MLS) techniques. The proposed approach uses the attributes of the 3D points to generate and match 2D-projections, by employing a simple correlation technique instead of matching in 3D. As a result, the developed method depends more on the number of pixels in the 2D-projections and less on the number of points in the point clouds. This leads to a more cost-efficient method in contrast to 3D registration techniques. The method uses this benefit to provide redundant translation parameters for each point cloud pair. With the utilization of image-based evaluation criteria the reliable translation parameters are detected and only those are used to compute the final solution. Consequently, the confidence levels of each final estimation can be computed. In addition, an indication of robustness showing how many estimations where included for the computation of the final solution is included. It is shown that the method performs fast due to its simplicity especially when medium image resolution’s such as 0.15m are used. Reliable matches can be produced even when the overlap of the point cloud sets is small or the initial offset large as long as the offsets are distinguishable in the projections. Furthermore, a technique is proposed to obtain capabilities for sub-pixel accuracy estimations, as the accuracy of the estimations is restricted to the grid cell size. The technique seems promising, but further improvement is necessary.
Original language | English |
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Pages (from-to) | 161-168 |
Number of pages | 8 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Volume | 42 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2018 |
Event | ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change” - Delft, Netherlands Duration: 1 Oct 2018 → 5 Oct 2018 |
Keywords
- Image registration
- Mobile scanning
- Pairwise registration
- Point clouds
- Relative registration
- Reliability
- Sub-pixel accuracy
- Template matching