Fusing CAMs-weighted features and temporal information for robust loop closure detection

As a key component in simultaneous localization and mapping (SLAM) system, loop closure detection (LCD) eliminates the accumulated errors by recognizing previously visited places. In recent years, deep learning methods have been proved effective in LCD. However, most of the existing methods do not make good use of the useful information provided by monocular images, which tends to limit their performance in challenging dynamic scenarios with partial occlusion by moving objects. To this end, we propose a novel workflow, which is able to combine multiple information provided by images. We first introduce semantic information into LCD by developing a local-aware Class Activation Maps (CAMs) weighting method for extracting features, which can reduce the adverse effects of moving objects. Compared with previous methods based on semantic segmentation, our method has the advantage of not requiring additional models or other complex operations. In addition, we propose two effective temporal constraint strategies, which utilize the relationship of image sequences to improve the detection performance. Moreover, we propose to use the keypoint matching strategy as the final detector to further refuse false positives. Experiments on four publicly available datasets indicate that our approach can achieve higher accuracy and better robustness than the state-of-the-art methods.