A Robust and Lightweight Loop Closure Detection Approach for Challenging Environments

Loop closure detection is crucial for simultaneous localization and mapping (SLAM), as it can effectively correct the accumulated errors. Complex scenarios put forward high requirements on the robustness of loop closure detection. Traditional feature-based loop closure detection methods often fail to meet these challenges. To solve this problem, this paper proposes a robust and efficient deep-learning-based loop closure detection approach. We employ MixVPR to extract global descriptors from keyframes and construct a global descriptor database. For local feature extraction, SuperPoint is utilized. Then, the constructed global descriptor database is used to find the loop frame candidates, and LightGlue is subsequently used to match the most similar loop frame and current keyframe with the local features. After matching, the relative pose can be computed. Our approach is first evaluated on several public datasets, and the results prove that our approach is highly robust to complex environments. The proposed approach is further validated on a real-world dataset collected by a drone and achieves accurate performance and shows good robustness in challenging conditions. Additionally, an analysis of time and memory costs is also conducted and proves that our approach can maintain accuracy and have satisfactory real-time performance as well.