Feature-based Registration of LiDAR Point Clouds
Date Issued
2012
Date
2012
Author(s)
Chuang, Tzu-Yi
Abstract
As a developing 3-D surface measurement and mapping technology, LiDAR (light detection and ranging) continues to attract research attention in many application areas. Registration of overlapping LiDAR point clouds is typically required and constitutes an essential data processing step to form a comprehensive 3-D scene. Point clouds being registered may well be strips from aerial LiDAR, routes from mobile LiDAR or individual static terrestrial scans. Indeed, the required registration may also be between different LiDAR platforms, aerial and terrestrial LiDAR for example. In general, registration can be carried out with the aid of artificial markers, utilizing surface matching approaches or employing geometric features implied within point clouds. Research about integrating multiple feature matching that provides choices as to the optimal configuration against geometric scene restrictions is currently limited, and integrated schemes which comprise the extraction of features, matching and estimation of transformation parameters without the aid of artificial markers and initial approximations have yet to be reported. Moreover, most existing registration methods are designed for a single type of LiDAR and do not fully support cross-platform registration.
This research proposed a feature-based approach for LiDAR point cloud registration, which can efficiently handle registration between point clouds from both single- and multi-platform LiDAR without the aid of either markers or the provision of initial approximations for 3-D transformation parameters. Depending upon the geometric characteristics of the point clouds to be registered, primary features, including points, lines and planes, are employed. Each feature type can be used either exclusively or in combined fashion. The proposed working scheme comprises three kernels, namely a feature extractor for feature acquisition, a RSTG approach as a feature matching technique and, finally, a robust transformation model for the estimation of transformation parameters. It should be noted that these three individual parts are not necessarily tied to each other, that is, each means included in the study can be working independently in the scope of this research or cooperated with other applications. Furthermore, this study investigated into the effects upon registration quality in terms of the multiple feature integration, adjustment models for the estimation of transformation parameters and the choice of a reference frame. It would give an in-depth exploration of the optimization of data processing as well as the benefits of multiple features in a registration task. This study has been proven accomplishing a comprehensive, highly flexible and effective point cloud registration working approach.
Subjects
LiDAR
Geo-feature
Extraction
Matching
Registration
Type
thesis
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