Loop closure detection is an effective strategy to reduce the pose drift of lidar-inertial simultaneous localization and mapping(SLAM),and the accuracy and speed of loop closure detection are key factors for its application in SLAM.Based on this,a lidar-inertial SLAM algorithm based on triangle bag of words loop closure detection is proposed.Firstly,a triangle descriptors are generated by the LinK3D features of the laser point clouds,and a triangle word bag is constructed by using the triangle descriptors to achieve real-time position recognition and six-degree-of-freedom loop pose estimation.Secondly,LinK3D features can also be used for frame to frame point cloud registration,combined with inertial measurement unit(IMU)pre-integration to achieve accurate and robust interframe pose estimation.The experimental results on the KITTI dataset show that compared with the current advanced LIO-SAM algorithm,the proposed SLAM algorithm has a more robust interframe pose estimation,the average root mean square error of the output trajectory is reduced by 29.79％,and the average time required for each loop constraint is reduced by 93.53％.The field experimental results show that compared with LIO-SAM,the proposed algorithm reduces the average time required for each loop constraint by 85.15％,and the root mean square error of the absolute trajectory error in outdoor long-distance experiments is reduced by 84.36％.
simultaneous localization and mappingloop closure detectionbag of wordspoint cloud registration
维普
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