Probability iterative closest point algorithm for m-D point set registration with noise

Du, Shaoyi; Liu, Juan; Zhang, Chunjia; Zhu, Jihua; Li, Ké

doi:10.1016/j.neucom.2015.01.019

Cited by 88 publications

(28 citation statements)

References 30 publications

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“…These are the points identified as identical in the areas of overlap for two or more scans being mutually registered. The MSA method uses the iterative closest points (ICP) algorithm which minimises the 3D distance between the polydata points by translating and/or rotating the entire point cloud along X, Y, and Z axes until the least possible distance between the polydata points is reached [39,45,46] ( Figure 6). The TLS point clouds (scans) were registered in such a way that the position and orientation of the first scan were locked and the second scan was translated and rotated until the smallest standard deviation was reached with respect to the polydata of first scan.…”

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

“…The MSA method identified only several tens of identical points (i.e., polydata) between the TLS and UAV-SfM point clouds in areas of markedly differing point densities. The automatic registration process was performed in several steps, with each step further reducing the values of the search window parameters and the point cloud rotation angle [39,45,46]. By decreasing the values of these parameters, the registration error was reduced from decimeters to several centimeters.…”

Section: Tls and Uav-sfm Data Fusionmentioning

confidence: 99%

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Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

et al. 2019

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Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.The general advantage of laser scanning over photogrammetry is in the ability of sampling several kinds of surfaces (e.g., top of vegetation canopy, inter-canopy surfaces, and ground) which are in the line of sight of the laser beam until impermeable surface restrains further penetration of the laser energy. UAV-SfM has become a low-cost alternative to TLS, generating point clouds with comparable accuracies to TLS albeit with the limitation of sampling the top surface in the field of view. Either way, in cases where TLS or UAV-SfM are used separately, data shadows originate in areas which are obscured in the sensor's field of view [23,29] (Figure 1). Complementing the unsampled areas with the surface altitude measurements is possible by sensing the area from multiple positions and different viewing perspectives by changing the location of the sensor. Zhang and Lin [30] provide a systematic overview of the lidar and photogrammetric data fusion. Cawood et al. [7] showed there is no exclusive method for capturing complex 3D geometry in the case study of 3D modelling a large boulder with distinct geological structural features. Combination of TLS and UAV-SfM provided the most satisfying results to capture the geometric complexity of the object of interest for structural analysis. A similar approach of combining different viewing geometry of TLS and photogrammetry was demonstrated in 3D modelling of a historical city by [23]. Planning the data collection in the mentioned studies concerned environments with a relatively easy access for the technology and the surveying pe...

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Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Section: Tls and Uav-sfm Data Fusionmentioning

confidence: 99%

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

et al. 2019

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“…In recent years, this notion has been widely applied in signal processing [9,10,24,25,27,22], computer vision [5,6,7], and image processing [12,13,14]. In [9,10], Fiori and Tanaka applied the Karcher mean to compute the principal components and independent components of data sets by the steepest descent method on special orthogonal group SO(n), which was also done independently by Manton [24].…”

Section: Introductionmentioning

confidence: 99%

Compute Karcher means on SO(n) by the geometric conjugate gradient method

et al. 2016

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“…Moreover, a great number of scholars devote great efforts to enhancing the performance of ICP on the robustness and speed. For instance, to improve the robustness, the improvement of robustness was realized [8] by introducing a new hybrid genetic algorithm technique and evaluation metric based on surface interpenetration, and a probability ICP algorithm was proposed for rigid registration of point sets with noise [9]. Meanwhile, Kim et al [10] improved the speed with two acceleration techniques which were hierarchical model point selection and logarithmic data point search.…”

Section: Introductionmentioning

confidence: 99%

Accurate non-rigid registration based on heuristic tree for registering point sets with large deformation

Liu

Zhang

et al. 2015

Neurocomputing

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Probability iterative closest point algorithm for m-D point set registration with noise

Cited by 88 publications

References 30 publications

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Compute Karcher means on SO(n) by the geometric conjugate gradient method

Accurate non-rigid registration based on heuristic tree for registering point sets with large deformation

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