Point Cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

Abstract

The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point cloud. In this paper, to address the semantic segmentation for safety driving of unmanned shuttle buses, an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios (such as campus). Firstly, we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation, and then propose an improved semantic segmentation model based on Cylinder3D, which improves mean Intersection over Union (mIoU) by 1.3% over the original model on SemanticKITTI data; then, a semantic category division method is proposed for road scenario of shuttle bus and practical application requirements, and then we further simplify the model to improve the efficiency without losing the accuracy. Finally, the nuScenes dataset and the real gathered campus scene data are used to validate and analyze the proposed method. The experimental results on the nuScenes dataset and our data demonstrate that the proposed method performs better than other point cloud semantic segmentation methods in terms of application requirements for unmanned shuttle buses. Which has a higher accuracy (82.73% in mIoU) and a higher computational efficiency (inference speed of 90 ms).