RLS-LCD: An Efficient Loop Closure Detection for Rotary-LiDAR Scans

Loop closure detection (LCD) is a crucial technology within the field of simultaneous localization and mapping (SLAM), aiming to mitigate cumulative errors and maintain a globally consistent map. Traditional fixed-Light Detection and Ranging (LiDAR) scans have demonstrated reliable LCD performance when employing global descriptors. However, for rotary-LiDAR scans, these methods fall short in place recognition due to significant view-angle changes. To address this challenge, we propose a novel LCD algorithm tailored for rotary-LiDAR scans, named RLS-LCD. Our RLS-LCD approach utilizes submap-based recognition and a novel lightweight global descriptor to mitigate the impact of large view-angle changes inherent to rotary-LiDAR scans. Additionally, we adopt a coarse-to-fine (C2F) recognition strategy to enhance recall and precision of place recognition in similar structure scenes. The experimental findings unequivocally demonstrate that the RLS-LCD algorithm not only outperforms the contemporary LCD methodologies reliant on global descriptors but also exhibits the capability to execute in real time on computing platforms with limited computational resources.