LiDAR-based HD Map Localization using Semantic Generalized ICP with Road Marking Detection
<|reference_start|>LiDAR-based HD Map Localization using Semantic Generalized ICP with Road Marking Detection: In GPS-denied scenarios, a robust environmental perception and localization system becomes crucial for autonomous driving. In this paper, a LiDAR-based online localization system is developed, incorporating road marking detection and registration on a high-definition (HD) map. Within our system, a road marking detection approach is proposed with real-time performance, in which an adaptive segmentation technique is first introduced to isolate high-reflectance points correlated with road markings, enhancing real-time efficiency. Then, a spatio-temporal probabilistic local map is formed by aggregating historical LiDAR scans, providing a dense point cloud. Finally, a LiDAR bird's-eye view (LiBEV) image is generated, and an instance segmentation network is applied to accurately label the road markings. For road marking registration, a semantic generalized iterative closest point (SG-ICP) algorithm is designed. Linear road markings are modeled as 1-manifolds embedded in 2D space, mitigating the influence of constraints along the linear direction, addressing the under-constrained problem and achieving a higher localization accuracy on HD maps than ICP. Extensive experiments are conducted in real-world scenarios, demonstrating the effectiveness and robustness of our system.<|reference_end|>
@article{gong2024lidar-based,
    title={LiDAR-based HD Map Localization using Semantic Generalized ICP with Road
  Marking Detection},
    author={Yansong Gong, Xinglian Zhang, Jingyi Feng, Xiao He and Dan Zhang},
    journal={arXiv preprint arXiv:2407.02061},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02061},
    primaryClass={cs.RO}
}