Why ORB-SLAM is missing commonly occurring loop closures?

Abstract

We analyse, for the first time, the popular loop closing module of a well known and widely used open-source visual SLAM (ORB-SLAM) pipeline. Investigating failures in the loop closure module of visual SLAM is challenging since it consists of multiple building blocks. Our meticulous investigations have revealed a few interesting findings. Contrary to reported results, ORB-SLAM frequently misses large fraction of loop closures on public (KITTI, TUM RGB-D) datasets. One common assumption is, in such scenarios, the visual place recognition (vPR) block of the loop closure module is unable to find a suitable match due to extreme conditions (dynamic scene, viewpoint/scale changes). We report that native vPR of ORB-SLAM is not the sole reason for these failures. Although recent deep vPR alternatives achieve impressive matching performance, replacing native vPR with these deep alternatives will only partially improve loop closure performance of visual SLAM. Our findings suggest that the problem lies with the subsequent relative pose estimation module between the matching pair. ORB-SLAM3 has improved the recall of the original loop closing module. However, even in ORB-SLAM3, the loop closing module is the major reason behind loop closing failures. Surprisingly, using off-the-shelf ORB and SIFT based relative pose estimators (non real-time) manages to close most of the loops missed by ORB-SLAM. This significant performance gap between the two available methods suggests that ORB-SLAM’s pipeline can be further matured by focusing on the relative pose estimators, to improve loop closure performance, rather than investing more resources on improving vPR. We also evaluate deep alternatives for relative pose estimation in the context of loop closures. Interestingly, the performance of deep relocalization methods (e.g. MapNet) is worse than classic methods even in loop closures scenarios. This finding further supports the fundamental limitation of deep relocalization methods recently diagnosed. Finally, we expose bias in well-known public dataset (KITTI) due to which these commonly occurring failures have eluded the community. We augment the KITTI dataset with detailed loop closing labels. In order to compensate for the bias in the public datasets, we provide a challenging loop closure dataset which contains challenging yet commonly occurring indoor navigation scenarios with loop closures. We hope our findings and the accompanying dataset will help the community in further improving the popular ORB-SLAM’s pipeline.