3D Keypoint Repeatability for Heterogeneous Multi-Robot SLAM

Abstract

For robots with different types of sensors, loop closure in a multi-robot SLAM scenario requires keypoints that can be matched between sensor measurement point clouds with different properties such as point density and noise. In this paper, we evaluate the performance of several 3D keypoint detectors (Harris3D, ISS, KPQ, KPQ-SI, and NARF) for repeatability between scans from different sensors towards building a heterogeneous multi-robot SLAM system. We find that KPQ-SI and NARF have the best relative repeatability, with KPQ-SI finding more keypoints overall and a higher number of repeatable keypoints, at the cost of significantly worse computational performance. In scans of the same area from different poses, both detectors find enough keypoints for point cloud registration and loop closure. For heterogenous multirobot SLAM applications with computational or bandwidth restrictions, the NARF detector consistently finds repeatable keypoints while also allowing for real-time performance.