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Remote Sens. 2017, 9(3), 281; doi:10.3390/rs9030281

Multi-Feature Registration of Point Clouds

Department of Civil Engineering, National Taiwan University, Taipei City, Taiwan 10617
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Author to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Richard Gloaguen and Prasad S. Thenkabail
Received: 23 October 2016 / Revised: 31 December 2016 / Accepted: 12 March 2017 / Published: 16 March 2017
(This article belongs to the Special Issue Airborne Laser Scanning)

Abstract

Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data. View Full-Text
Keywords: LiDAR; multiple features; registration; simultaneous adjustment; cross-platform LiDAR; multiple features; registration; simultaneous adjustment; cross-platform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chuang, T.-Y.; Jaw, J.-J. Multi-Feature Registration of Point Clouds. Remote Sens. 2017, 9, 281.

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