Abstract
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.
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