Gaussian Scan Context: A Statistical Global Descriptor for Reliable Loop Closure Detection

Abstract

Loop Closure Detection is a fundamental component of any SLAM system. By performing place recognition, a robot can correct accumulated drift errors arising from odometry uncertainties during the mapping process. Numerous techniques have been proposed in the literature to address this task under different sensor configurations, including RGB cameras and LiDAR. Among these, LiDAR-based SLAM has gained substantial attention due to its robustness in outdoor environments and its invariance to illumination changes. However, LiDAR sensors inherently provide less texture information compared to cameras, introducing additional challenges for loop closure detection. One of the most widely adopted approaches in LiDAR-based SLAM is Scan Context, recognized for its simplicity and effectiveness. This method has been successfully integrated into a broad range of applications and algorithms. Nevertheless, its simplicity can also lead to reduced robustness when no supplementary verification mechanisms are employed. In this work, we introduce Gaussian Scan Context, an enhancement to the original Scan Context that incorporates statistical analysis of the input point cloud. This approach accounts for the distribution of points within each context bin. Experimental results demonstrate that this enhancement improves both robustness and overall performance, as supported by various metrics.