LiDAR Iris for Loop-Closure Detection

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.

I. INTRODUCTION

During the past years, many techniques have been introduced that can perform simultaneous localization and mapping using both regular cameras and depth sensing technologies based on either structured light, ToF, or Li-DAR. Even though these sensing technologies are producing accurate depth measurements, however, they are still far from perfect. As the existing methods cannot completely eliminate the accumulative error of pose estimation, these methods still suffer from the drift problem. These drift errors could be corrected by incorporating sensing information taken from places that have been visited before, or loop-closure detection, which requires algorithms that are able to recognize revisited areas. Unfortunately, existing solutions for loop detection for 3D LiDAR data are not both robust and fast enough to meet the demand of real-world SLAM applications.