LiDAR Iris for Loop-Closure Detection
This addresses loop-closure detection for autonomous navigation systems, but it is incremental as it builds on existing descriptor-based methods.
The paper tackles loop-closure detection in LiDAR point clouds by proposing LiDAR Iris, a global descriptor that uses binary signature images and Hamming distance for fast and accurate matching, achieving excellent performance on five road-scene sequences.
In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x,y,yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.