Zhang Yi, Dong Feiyang, Sun Qihao, Song Weiwei
School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China.
GNSS Research Center, Key Laboratory of Luojia of Hubei Province, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2025 Jun 12;25(12):3681. doi: 10.3390/s25123681.
When facing geometrically similar environments such as subway tunnels, Scan-Map registration is highly dependent on the correct initial value of the pose, otherwise mismatching is prone to occur, which limits the application of SLAM (Simultaneous Localization and Mapping) in tunnels. We propose a novel coarse-to-fine registration strategy that includes geometric feature extraction and a keyframe-based pose optimization model. The method involves initial feature point set acquisition through point distance calculations, followed by the extraction of line and plane features, and convex hull features based on the normal vector's change rate. Coarse registration is achieved through rotation and translation using three types of feature sets, with the resulting pose serving as the initial value for fine registration via Point-Plane ICP. The algorithm's accuracy and efficiency are validated using Innovusion lidar scans of a subway tunnel, achieving a single-frame point cloud registration accuracy of 3 cm within 0.7 s, significantly improving upon traditional registration algorithms. The study concludes that the proposed method effectively enhances SLAM's applicability in challenging tunnel environments, ensuring high registration accuracy and efficiency.
当面对几何形状相似的环境，如地铁隧道时，Scan-Map配准高度依赖于位姿的正确初始值，否则容易出现匹配错误，这限制了SLAM（同时定位与地图构建）在隧道中的应用。我们提出了一种新颖的从粗到精的配准策略，该策略包括几何特征提取和基于关键帧的位姿优化模型。该方法首先通过点距离计算获取初始特征点集，然后提取线、面特征以及基于法向量变化率的凸包特征。通过使用三种类型的特征集进行旋转和平移实现粗配准，得到的位姿作为通过点-面ICP进行精配准的初始值。使用Innovusion激光雷达对地铁隧道的扫描数据验证了该算法的准确性和效率，在0.7秒内实现了单帧点云配准精度达到3厘米，显著优于传统配准算法。研究得出结论，所提出的方法有效地提高了SLAM在具有挑战性的隧道环境中的适用性，确保了高配准精度和效率。
