Robust Stereo Road Image Segmentation Using Threshold Selection Optimization Method Based on Persistent Homology

This paper introduces a novel method for road target segmentation in the context of autonomous driving based on stereo disparity maps. The proposed method utilizes topological persistence threshold analysis to address the challenges of selecting appropriate thresholds. The approach involves converting stereo road images into uv-disparity maps, extracting road planes using v-disparity maps, and calculating occupancy grid maps using u-disparity maps. Persistence diagrams are then constructed by generating segmentation results under various threshold parameters. By establishing persistence boundaries in these diagrams, the most significant regions are identified, enabling the determination of robust segmentation thresholds. Experimental validation using KITTI stereo image datasets demonstrates the effectiveness of the proposed method, with low error rates and superior performance compared to other segmentation methods. The research holds potential for application in autonomous driving systems.

Automated summary

This paper presents a novel method for road target segmentation in autonomous driving based on stereo disparity maps. The proposed method addresses the challenge of selecting appropriate thresholds by using topological persistence threshold analysis. Experimental validation demonstrates the effectiveness and superior performance of the method, suggesting potential for application in autonomous driving systems.