SAIL-MAP: Loop-closure detection using saliency-based features

Abstract

Loop-closure detection, which is the ability to recognize a previously visited place, is of primary importance for robotic localization and navigation problems. We here introduce SAIL-MAP, a method for loop-closure detection based on vision only, applied to topological simultaneous localization and mapping (SLAM). Our method allows the matching of camera images using a novel saliency-based feature detector and descriptor. These features have been designed to benefit from the robustness to viewpoint change and image perturbations of bio-inspired saliency algorithms. Additionally, the same algorithm is used for the detector and descriptor. The results obtained on different large-scale data sets demonstrate the efficiency of the proposed solution for localization problems.