Visual Loop-Closure Detection via Prominent Feature Tracking

Loop-closure detection (LCD) has become an essential part of any simultaneous localization and mapping (SLAM) framework. It provides a means to rectify the drift error, which is typically accumulated along a robot’s trajectory. In this article we propose an LCD method based on tracked visual features, combined with a signal peak-trace filtering approach for loop-closure identification. In particular, local binary features are firstly extracted and tracked through consecutive frames. This way online visual words are generated, which in turn form an incremental bag of visual words (BoVW) vocabulary. Loop-closures (LCs) result from a classification method, which considers current and past state peaks on the similarity matrix. The system discerns the movement of the peaks to identify whether they come about to be true-positive detections or background noise. The suggested peak-trace filtering technique provides exceeding robustness to noisy signals, enabling the usage of only a handful of visual local features per image; thus resulting into a considerably downsized visual vocabulary.