Three-dimensional point cloud plane segmentation in both structured and unstructured environments

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Abstract

This paper focuses on three-dimensional (3D) point cloud plane segmentation. Two complementary strategies are proposed for different environments, i.e., a subwindow-based region growing (SBRG) algorithm for structured environments, and a hybrid region growing (HRG) algorithm for unstructured environments. The point cloud is decomposed into subwindows first, using the points' neighborhood information when they are scanned by the laser range finder (LRF). Then, the subwindows are classified as planar or nonplanar based on their shape. Afterwards, only planar subwindows are employed in the former algorithm, whereas both kinds of subwindows are used in the latter. In the growing phase, planar subwindows are investigated directly (in both algorithms), while each point in nonplanar subwindows is investigated separately (only in HRG). During region growing, plane parameters are computed incrementally when a subwindow or a point is added to the growing region. This incremental methodology makes the plane segmentation fast. The algorithms have been evaluated using real-world datasets from both structured and unstructured environments. Furthermore, they have been benchmarked against a state-of-the-art point-based region growing (PBRG) algorithm with regard to segmentation speed. According to the results, SBRG is 4 and 9 times faster than PBRG when the subwindow size is set to 3×3 and 4×4 respectively; HRG is 4 times faster than PBRG when the subwindow size is set to 4×4. Open-source code for this paper is available at https://github.com/junhaoxiao/TAMS-Planar- Surface-Based-Perception.git. © 2013 Elsevier B.V. All rights reserved.

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Xiao, J., Zhang, J., Adler, B., Zhang, H., & Zhang, J. (2013). Three-dimensional point cloud plane segmentation in both structured and unstructured environments. Robotics and Autonomous Systems, 61(12), 1641–1652. https://doi.org/10.1016/j.robot.2013.07.001

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