3D Segment-Based Road Boundary Extraction Method via Spatio-Temporal Analysis

Accurate and effective road boundary extraction plays a significant role in the navigation and decision-making processes of self-driving cars. Nevertheless, the reliable detection of road boundaries via 3D LiDAR is particularly difficult due to uneven point cloud density and chaotic vegetation areas. Conventional methods often require time-consuming fitting or clustering algorithms to enhance performance. To this end, this paper presents a road boundary extraction approach particularly focuses on the curbs and vegetation areas, utilizing LiDAR data without fitting or clustering, enabling the generation of detailed road maps. We exploit both single and multiple frames of data in the design, which enables spatio-temporal feature extraction. This strategy is realized by integrating the road boundary detection algorithm and the SLAM technology. The algorithm contains three stages: 1) Coarse Ground Segmentation (CGS), 2) Adaptive Spatial Feature Extraction (A-SFE), 3) Iterative Multi-scale Refinement (I-MSR). Experiments on the KITTI dataset are conducted for verification. The proposed method not only outperforms traditional methods with an average of 85% in key metrics but also demonstrates comparable performance to state-of-the-art deep learning models.