Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
This addresses the problem of improving place recognition accuracy for robotics and autonomous systems, though it is incremental by adding intensity to existing geometric approaches.
The paper tackles loop closure detection in SLAM by proposing a novel global descriptor that incorporates both geometry and intensity from LiDAR scans, achieving higher recall rates and precision compared to existing geometric-only methods.
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage the matching of local or global geometrical-only descriptors, but without considering the intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. Concretely, we propose a novel global descriptor, intensity scan context (ISC), that explores both geometry and intensity characteristics. To improve the efficiency for loop closure detection, an efficient two-stage hierarchical re-identification process is proposed, including a binary-operation based fast geometric relation retrieval and an intensity structure re-identification. Thorough experiments including both local experiment and public datasets test have been conducted to evaluate the performance of the proposed method. Our method achieves higher recall rate and recall precision than existing geometric-only methods.