G2P-SLAM: Generalized RGB-D SLAM Framework for Mobile Robots in Low-Dynamic Environments

In this paper, we propose a generalized grouping and pruning method for RGB-D SLAM in low-dynamic environments. The conventional grouping and pruning methods successfully reject the effect of dynamic objects in pose graph optimization (PGO). However, these methods sometimes fail when high-dynamic objects are dominant in the images captured by RGB-D sensors. Furthermore, once it is determined whether the features from dynamic objects are included in some nodes, the corresponding nodes are entirely removed even though these nodes partially include true constraints, which leads to an inaccurate PGO. To tackle these problems, we propose a novel method with intra-grouping, inter-grouping, and selective pruning, called G2P-SLAM. Accordingly, our method successfully rejects false constraints from dynamic objects selectively, thus preserving true constraints from static objects as many as possible. As experimentally verified on both our own datasets and public datasets, our proposed method shows promising performance compared with the state-of-the-art methods. Furthermore, experimental results corroborate that our G2P-SLAM enables robust PGO in both dynamic and low-dynamic environments.