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.
Highlights
Light detection and ranging (LiDAR) has been an effective technique for obtaining dense and accurate 3-D point clouds
Real scenes with feature appearances collected by terrestrial LiDAR system (TLS), airborne LiDAR system (ALS) and mobiles LiDAR system (MLS) that dealt with both single- and cross-platform registrations were subsequently demonstrated to reveal the feasibility and effectiveness of the proposed method
In addition to employing check features, we introduced the registered features into Equation (15) to derive internal accuracy (IA), which has the same meaning as the mean square error (MSE) index of the iterative closest point (ICP) algorithm [3]
Summary
Light detection and ranging (LiDAR) has been an effective technique for obtaining dense and accurate 3-D point clouds. It drew our attention to the fact that the existing studies of feature-based point cloud registration employing line and plane features either utilized only partial geometric information [13,28]. Considering the point, line, and plane features, the transformation model was driven by the existing models to improve the exploitation of features on both geometric and weighting aspects and to offer a simultaneous adjustment for global point cloud registration. To correctly evaluate the random effect of feature observations derived from the different quality of point cloud data, the variance–covariance matrix of the observations is considered based on the fidelity of data sources. Only straight line feature throughout the paragraphs different point cloud data,employs the variance–covariance matrix of the observations is that follow. The transformation parameters among multiple datasets are simultaneously determined via a weighted least-squares adjustment
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