Multi-Feature 3D Road Point Cloud Semantic Segmentation Method Based on Convolutional Neural Network

Aiming at the problem of low accuracy in semantic segmentation of three-dimensional laser point clouds in road scene, an end-to-end multi-feature point clouds semantic segmentation method based on convolutional neural network is proposed. Firstly, the feature images such as point cloud distance, adjacent angle and surface curvature arc calculated based on spherical projection to apply to convolutional neural network; then, a convolutional neural network is adopted to process multi-band depth images to obtain pixel-level instance segmentation results. The proposed method combines traditional point cloud features with the deep learning method to improve the result of point cloud semantic segmentation. Using KITTI point cloud data set test, simulation results show that the multi feature convolutional neural network semantic segmentation method has better performance than other semantic segmentation methods without combining with point cloud features such as SqueezeSeg V2. The precision obtained with proposed method for car, bicycle and pedestrian segmentation is 0.3, 21.4, 14.5 percentage points higher in comparison with the SqueezeSeg V2 network.