SANet:real time semantic segmentation method of LiDAR point cloud based on spatial attention mechanism

doi:10.13474/j.cnki.11-2246.2022.0321

Abstract

Abstract: Semantic segmentation is an important basis for intelligent robots to move from perceptual intelligence to cognitive intelligence. The current semantic segmentation methods for point cloud data have poor real-time performance and low accuracy. In this article, we systematically analyze the difference between the range images generated by spherical projection of point cloud and common images, and provide ideas for the design of real-time semantic segmentation neural network. Through the analysis, we find that the range images have the characteristics of strong spatial correlation. This article combines the strong spatial correlation with attention mechanism, then proposes a real-time semantic segmentation method SANet based on spatial attention mechanism. SANet can efficiently aggregate spatial distribution features and context features. And the model parameters are less, which can meet the real-time requirements. Experiments on the SemanticKITTI dataset show that SANet has both good real-time performance and high accuracy compared with other excellent algorithms. The spatial attention mechanism proposed in this article significantly improves the accuracy of semantic segmentation of LiDAR point cloud by efficiently aggregating spatial distribution features and context features, which can provide auxiliary support for autonomous driving and other robot applications.

Key words: spatial attention, semantic segmentation of point cloud, SemanticKITTI, range image

CLC Number:

P237

WANG Weiqi, YOU Xiong, SU Mingzhan, ZHANG Lantian, ZHOU Xueying, ZHAO Yao. SANet:real time semantic segmentation method of LiDAR point cloud based on spatial attention mechanism[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 32-38.

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