Abstract
Loop closing is an essential task for SLAM system, which can reduce the pose drifts and map distortion. However, challenges from viewpoint changes and sparse-channel lidar are still open problems. In this paper, a novel efficient and viewpoint-invariant loop closing method is presented, which well fits sparse-channel lidars. To keep the metric information of 3D point clouds, elevation maps are explored to represent sparse point clouds, based on which a novel and viewpoint-invariant key point feature detection method is proposed. To improve efficiency, Bag of Words technique is applied to accelerate the process of scene description and matching. In addition, a strict geometric consistency check of matched key point pairs is proposed to reject false positive detections, which makes our method robust to scenes with similar layouts in urban environments. Extensive experiments on challenging public datasets verify the validity and superiority of the proposed method.
Published Version
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