Research on Human Features Semantic Segmentation Based on Laser Point Cloud

Abstract

In view of the problems of health care for the semi-disabled elderly, this paper studies the semantic segmentation of human features in a bathing environment with a scrubbing device. Firstly three-dimensional point cloud data of different types to construct a human model is collected by the lidar. Secondly, overcome the influence of the water fog environment on the modeling by the hybrid filtering algorithm, and the human point cloud area is extracted. Finally, the human semantic segmentation model fusing the spatial feature extraction module and the channel attention module is proposed based on PointNet improvement. After training and testing on the target data set, the results show that the algorithm can accurately identify feature information for 3D human models of different types. The segmentation rate reaches 95.7%, which is 4.5% higher than the PointNet network, significantly improves the segmentation of human features, and has high engineering application value.