电子测量与仪器学报

张冰战,尹晨晨,李志远,邹明明.基于点云特征全局搜索的回环检测算法[J].电子测量与仪器学报,2024,38(3):176-186

基于点云特征全局搜索的回环检测算法

Loop closure detection algorithm based on global search of point cloud features

DOI：

英文关键词:laser SLAM loop detection positioning feature descriptor mobile robot

基金项目:中央高校基本科研业务费专项资金资助（PA2023GDSK0065)、2023年芜湖市科技计划项目（科技局）(2223jc04)资助

作者	单位
张冰战	1.合肥工业大学汽车与交通工程学院合肥230009；2.安徽省数字化设计与制造重点实验室合肥230009
尹晨晨	合肥工业大学汽车与交通工程学院合肥230009
李志远	合肥工业大学汽车与交通工程学院合肥230009
邹明明	3.合肥工业大学机械工程学院合肥230009；4.汽车技术与装备国家地方联合工程研究中心合肥230009

Author	Institution
Zhang Bingzhan	1.School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009，China; 2.Anhui Provincial Key Laboratory of Digital Design and Manufacturing, Hefei 230009，China
Yin Chenchen	School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009，China
Li Zhiyuan	School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009，China
Zou Mingming	3. School of Mechanical Engineering， Hefei University of Technology，Hefei 230009，China；4. National and Local Joint Engineering Research Center for Automotive Technology and Equipment， Hefei University of Technology， Hefei 230009, China

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中文摘要:

针对纯激光SLAM算法定位漂移问题，提出一种基于点云特征描述子全局搜索的粗匹配回环检测算法。该算法首先采用基于图像距离的快速分割方法对激光点云进行地面点去除，基于点云曲率和关键点聚合算法实现了边缘特征提取和聚类，通过特征描述子生成算法得到当前帧点云的特征描述符，其次经过计算当前帧和历史帧的相似度评分完成全局匹配搜索实现对候选回环帧的选取，完成回环检测粗匹配过程；然后采用NICP算法进行当前帧与候选回环帧的精确匹配，从而完成回环检测过程；最后搭建了移动机器人实车平台，完成对校园数据集的采集，验证了本文算法的定位效果，通过对实车实验结果的分析可知，在实车采集的校园数据集上误差优化程度均值为13.15%，为了进一步验证本文算法的整体性能，在KITTI数据集进行测试对比，结果显示相比较Lego_loam和Lio-sam算法，本文所提算法在保证了运行效率的基础上，有效地改进了定位精度。

英文摘要:

Aiming at the localisation drift problem of pure laser SLAM algorithm, a coarse matching loopback detection algorithm based on the global search of point cloud feature descriptor is proposed. The algorithm firstly adopts the fast segmentation method based on image distance to remove ground points from the laser point cloud, implements edge feature extraction and clustering based on the point cloud curvature and key point aggregation algorithm, and obtains the feature descriptor of the point cloud in the current frame through the feature descriptor generation algorithm, then completes the global matching search by calculating the similarity scores of the current frame and the historical frames to achieve the selection of candidate looping frames, and completes the loopback detection. The coarse matching process is completed. Then the NICP algorithm is used to accurately match the current frame with the candidate loopback frame to complete the loopback detection process. Finally, the real vehicle platform of the mobile robot is built to complete the acquisition of the campus dataset to verify the positioning effect of this paper’s algorithm, and through the analysis of the results of the experiments on the real vehicle, it can be seen that the average value of the degree of optimisation of the error on the campus dataset acquired by the real vehicle is 13.15%. In order to further validate the overall performance of this paper’s algorithm, test comparisons are performed on the KITTI dataset, and the results show that compared with the Lego_loam and Lio-sam algorithms, the algorithm proposed in this paper effectively improves the localisation accuracy on the basis of guaranteeing the operational efficiency.

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