Integrating global and local image features for enhanced loop closure detection in RGB-D SLAM systems

Loop closure detection is essential for simultaneous localization and mapping systems to decrease accumulating drift of trajectory estimations. Robust loop closure detection is specifically important in large-scale mapping, but it gets more challenging as the mapping environment grows. This paper proposes a SLAM system utilizing a two-pass loop closure detection method to improve mapping accuracy in large-scale environments. The proposed system finds loop closure candidates by employing global and local image features together. After selecting a group of candidates by similarity of global features, the system applies keypoint matching on this group to improve scene matching accuracy and determines loop closure candidates. We extensively evaluate the system on the widely used TUM RGB-D dataset, which contains sequences of small to large-scale indoor environments, with respect to different parameter combinations. The results show that the proposed method increases accuracy substantially and achieves large-scale mapping with acceptable overhead.