Sequence-based mapping for probabilistic visual loop-closure detection

During simultaneous localization and mapping, the robot should build a map of its surroundings and simultaneously estimate its pose in the generated map. However, a fundamental task is to detect loops, i.e., previously visited areas, allowing consistent map generation. Moreover, within long-term mapping, every autonomous system needs to address its scalability in terms of storage requirements and database search. In this paper, we present a low-complexity sequence-based visual loop-closure detection pipeline. Our system dynamically segments the traversed route through a feature matching technique in order to define sub-maps. In addition, visual words are generated incrementally for the corresponding sub-maps representation. Comparisons among these sequences-of-images are performed thanks to probabilistic scores originated from a voting scheme. When a candidate sub-map is indicated, global descriptors are utilized for image-to-image pairing. Our evaluation took place on several publicly-available datasets exhibiting the system’s low complexity and high recall compared to other state-of-the-art approaches.