融合配准的多站室外大场景激光点云分割

doi:10.3969/j.issn.1000-1158.2022.03.05

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Abstract Aiming at the problem caused by the sparseness of outdoor laser point cloud scenes for semantic segmentation, a point cloud segmentation method based on deep learning is proposed. First, the laser point cloud scenes collected from five perspectives are processed, and parts of buildings with higher overlapping areas are selected in turn, and each group is registered by SAC-IA and ICP-based point cloud automatic registration methods. In order to construct a large outdoor scene with relatively uniform point cloud density, the public data set Semantic3D is used to train an outdoor point cloud segmentation model based on PointNet++, and the algorithm effect is verified on the test set. Finally, this model is used to segment the outdoor scene that has been constructed scenes and experimental results prove that point cloud scenes with multi-view registration can solve the problem of non-uniform sampling of point cloud scenes, so that the deep segmentation-based semantic segmentation model has a better recognition effect.

Key words： metrology laser point cloud scene segmentation fusion registration non-uniform sampling deep learning PointNet++

Received: 14 January 2021 Published: 23 March 2022

PACS:	TB96
	TB92

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	Articles by authors
	XU Peng
	XU Fang-yong
	CHEN Hui

Cite this article:

XU Peng,XU Fang-yong,CHEN Hui. Large Scene Segmentation of Outdoor Laser Point Cloud Based on Fusion and Registration[J]. Acta Metrologica Sinica, 2022, 43(3): 325-330.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.03.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I3/325

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