文献类型：会议论文

英文题名：A Hybrid Loop Closure Detection Method Based on Lidar SLAM

作者：Chai Mengna[1];Liu Yuansheng[2]

第一作者：Chai Mengna

通讯作者：Chai, MN[1]

机构：[1]Beijing Union Univ, Smart City Coll, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学继续教育学院

通讯机构：[1]corresponding author), Beijing Union Univ, Smart City Coll, Beijing, Peoples R China.|[1141733]北京联合大学继续教育学院;[11417]北京联合大学;

会议论文集：15th International Conference on Computational Intelligence and Security (CIS)

会议日期：DEC 13-16, 2019

会议地点：Macao, PEOPLES R CHINA

语种：英文

外文关键词：Autonomous vehicle; simultaneous localization and mapping; point cloud space location; appearance similarity; hybrid loop closure detection

摘要：Simultaneous localization and mapping (SLAM) is the key technology to realize self-localization of autonomous vehicles. However, the lidar SLAM with traditional loop closure detection cannot obtain reliable accuracy and real-time under large scale scene due to the accumulated error of point cloud registration. In this paper, a hybrid loop closure detection (HLCD) method based on spatial location and appearance similarity is proposed and introduced into lidar SLAM technology. An experimental test is implemented on the platform of autonomous vehicle to verify the correctness and feasibility of theoretical design. Experimental results show that the proposed method can effectively reduce the cumulative deviation and time consuming.