Gui Linqiu, Zeng Chunnian, Luo Jie, Yang Xu
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
School of Automation, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2024 Jun 3;24(11):3611. doi: 10.3390/s24113611.
Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%.
在封闭环境中运行的无人地面车辆（UGV）的自动驾驶系统严重依赖于带有先验地图的激光雷达定位。精确的初始位姿估计在系统启动期间或跟踪丢失时至关重要，可确保UGV的安全运行。现有的基于激光雷达的地点识别方法通常由于仅匹配来自单个激光雷达关键帧的描述符而导致精度降低。本文提出了一种基于隐马尔可夫模型（HMM）的多帧描述符匹配方法来解决这一问题。该方法通过利用多帧信息提高了地点识别的准确性和鲁棒性。来自KITTI数据集的实验结果表明，与基于扫描上下文的单帧描述符匹配方法相比，所提出的方法显著提高了地点识别性能，平均性能提高了5.8%，最大提高了15.3%。
