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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.
Tzu-Yi Chuang; Jen-Jer Jaw. Multi-Feature Registration of Point Clouds. Remote Sensing 2017, 9, 281 .
AMA StyleTzu-Yi Chuang, Jen-Jer Jaw. Multi-Feature Registration of Point Clouds. Remote Sensing. 2017; 9 (3):281.
Chicago/Turabian StyleTzu-Yi Chuang; Jen-Jer Jaw. 2017. "Multi-Feature Registration of Point Clouds." Remote Sensing 9, no. 3: 281.
Three‐dimensional (3D) feature‐matching techniques, which are essential for progress towards an automated feature‐based procedure, have attracted considerable research attention in both the photogrammetry and computer vision communities. This study introduces a novel matching approach, called RSTG, that comprises four major phases: rotation alignment; scale estimation; translation alignment; and geometry checks. These steps efficiently determine a feature‐based correspondence and frame transformation between datasets. RSTG analyses the similarity and relative geometry of features by employing feature observations and their uncertainty; this allows different types of features to be matched exclusively or simultaneously. This study validates the proposed method with both simulated and real datasets, demonstrating its effectiveness with satisfactory matching rates in a diverse range of feature‐based point cloud registration tasks.
Tzu-Yi Chuang; Jen-Jer Jaw. Automated 3d feature matching. The Photogrammetric Record 2015, 30, 8 -29.
AMA StyleTzu-Yi Chuang, Jen-Jer Jaw. Automated 3d feature matching. The Photogrammetric Record. 2015; 30 (149):8-29.
Chicago/Turabian StyleTzu-Yi Chuang; Jen-Jer Jaw. 2015. "Automated 3d feature matching." The Photogrammetric Record 30, no. 149: 8-29.
Techniques for extracting data from LiDAR point clouds can be refined for increased accuracy. In this paper, the authors elaborate on an innovative approach for registering ground‐based LiDAR point clouds using overlapping scans based on 3D line features. The proposed working scheme consists of three major kernels: a 3D line feature extractor, a 3D line feature matching mechanism, and a mathematical model for simultaneously registering ground‐based LiDAR point clouds of multi‐scans on a 3D line feature basis. All processing chains in this study are featured efficiently and come close to meeting the needs of practical usage. Experiments conducted show the proposed method of employing 3D line features to be a useful alternative or complement to point, surface and other features for LiDAR (Light Detection And Ranging) point clouds registration. It is especially effective in areas rich in man‐made structures.
Jen‐Jer Jaw; Tzu‐Yi Chuang. Registration of ground‐based LiDAR point clouds by means of 3D line features. Journal of the Chinese Institute of Engineers 2008, 31, 1031 -1045.
AMA StyleJen‐Jer Jaw, Tzu‐Yi Chuang. Registration of ground‐based LiDAR point clouds by means of 3D line features. Journal of the Chinese Institute of Engineers. 2008; 31 (6):1031-1045.
Chicago/Turabian StyleJen‐Jer Jaw; Tzu‐Yi Chuang. 2008. "Registration of ground‐based LiDAR point clouds by means of 3D line features." Journal of the Chinese Institute of Engineers 31, no. 6: 1031-1045.