Semantic Mapping of Incremental 3D Point Clouds Based on Multi-Hop Graph Attention Network

Mapping and Semantic mapping are the important research areas for the autonomous navigation of mobile robots. However, realising point cloud information extraction in a dynamic environment is still a challenge in semantic mapping. To solve this problem, We propose a method called PMGAT-SM(semantic m...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 2865 - 2869
Main Authors Chen, ShunTong, Xu, JiaChen, Yuan, Xia, Zhao, ChunXia
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Image edge detection
Image segmentation
Instance Segmentation
Mapping
Point cloud compression
Roads
Semantic Mapping
Semantics
Spread spectrum communication
Three-dimensional displays
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Summary:Mapping and Semantic mapping are the important research areas for the autonomous navigation of mobile robots. However, realising point cloud information extraction in a dynamic environment is still a challenge in semantic mapping. To solve this problem, We propose a method called PMGAT-SM(semantic mapping of 3D point clouds based on multi-hop graph attention network) for achieving semantic mapping in 3D point cloud environments. In PMGAT-SM, we designed a point cloud classifier PMGAT, which extracts semantic information of unordered point clouds by constructing a graph. We combined PMGAT with dynamic growing clustering to achieve instance segmentation in complex environments. Our extensive experiments in the KITTI scene show the effectiveness of the semantic mapping model. Meanwhile, compared with popular semantic information extraction models, the PMGAT performs better in fine-grained feature extraction of point cloud segments.
DOI:10.1109/ICIP49359.2023.10222008