Multi-Feature Registration of Point Clouds

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.

Automated 3d feature matching

“…A valid procedure for acquiring features is a prerequisite to any feature‐based technique. In this study, geometric features are automatically acquired by implementing a feature extractor (Chuang, ) on lidar point clouds, unless otherwise specified. The extractor provides the parameters and uncertainty of the features obtained, which can be used as input to RSTG.…”

Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

“…Such primitives have been employed to estimate the transformation parameters between two point clouds [41,[43][44][45][46][47][48][49][50][51][52]. Some studies demonstrated the ability of feature-based registration in handling point clouds acquired by different platforms, including airborne LiDAR, mobile LiDAR, TLSs, and even imagery [53][54][55][56][57][58]. Habib et al [59,60] registered LiDAR and photogrammetric data using linear features.…”

“…Pair-wise registration has two disadvantages: (i) it makes the process time-consuming when dealing with multiple scans and/or drive-runs; and (ii) the sequential registration leads to the propagation of errors, which increases as we move away from the reference scan. Furthermore, existing algorithms commonly utilize feature parameters (e.g., line endpoints, direction vector of linear/axis of cylindrical features, and normal vector of a plane) for the registration process [40,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. Direct use of these parameters would not allow for the sufficient mitigation of inherent noise and/or point density variations in the point cloud.…”

Nationwide Point Cloud—The Future Topographic Core Data

“…Co-registration by data becomes increasingly difficult if the viewpoints have less overlap [96]. Not only can data from several platforms be co-registered [97], but ALS can be used to remedy some positioning accuracy issues of overlapping MLS [49]. In a research setting it is possible to ensure that the environment remains sufficiently unchanged to facilitate co-registration.…”