Journal Article10.1002/ROB.21457
Planar Segment Based Three-dimensional Point Cloud Registration in Outdoor Environments
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TL;DR: Experimental results confirm that the approach offers an alternative to state‐of‐the‐art algorithms in plane‐rich environments and contains robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth.
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Abstract: We present an odometry-free three-dimensional (3D) point cloud registration strategy for outdoor environments based on area attributed planar patches. The approach is split into three steps. The first step is to segment each point cloud into planar segments, utilizing a cached-octree region growing algorithm, which does not require the 2.5D image-like structure of organized point clouds. The second step is to calculate the area of each segment based on small local faces inspired by the idea of surface integrals. The third step is to find segment correspondences between overlapping point clouds using a search algorithm, and compute the transformation from determined correspondences. The transformation is searched globally so as to maximize a spherical correlation-like metric by enumerating solutions derived from potential segment correspondences. The novelty of this step is that only the area and plane parameters of each segment are employed, and no prior pose estimation from other sensors is required. Four datasets have been used to evaluate the proposed approach, three of which are publicly available and one that stems from our custom-built platform. Based on these datasets, the following evaluations have been done: segmentation speed benchmarking, segment area calculation accuracy and speed benchmarking, processing data acquired by scanners with different fields of view, comparison with the iterative closest point algorithm, robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth. Experimental results confirm that the approach offers an alternative to state-of-the-art algorithms in plane-rich environments.
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Citations
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PLADE: A Plane-Based Descriptor for Point Cloud Registration With Small Overlap
TL;DR: This article proposes to use high-level structural information (i.e., plane/line features and their interrelationship) for registration, which is capable of registering point clouds with small overlap, allowing more freedom in data acquisition.
Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets
TL;DR: A semi-automatic and target-less method for coarse registration of point clouds using geometric constraints of voxel-based 4-plane congruent sets (V4PCS), which can be effective for registering point clouds acquired from various scenes and is more efficient than other baseline methods when using the same hardware and software configuration conditions.
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Three-dimensional point cloud plane segmentation in both structured and unstructured environments
TL;DR: These algorithms have been evaluated using real-world datasets from both structured and unstructured environments and benchmarked against a state-of-the-art point-based region growing (PBRG) algorithm with regard to segmentation speed.
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3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu,Steve Cousins +1 more
- 09 May 2011
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Scan registration for autonomous mining vehicles using 3D-NDT
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TL;DR: This paper presents a robotic mapping method based on locally consistent 3D laser range scans that combines Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system.
The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design
TL;DR: In this paper, the authors evaluate different variants of the Hough Transform with respect to their applicability to detect planes in 3D point clouds reliably, and present a novel approach to design the accumulator focusing on achieving the same size for each cell and compare it to existing designs.
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