Robust Loop-Closure Detection with a Learned Illumination Invariant Representation for Robot vSLAM

Abstract

Robust loop-closure detection plays a key role for the long-term robot visual Simultaneous Localization and Mapping(SLAM) in indoor or outdoor environment, due to illumination changes can greatly affect the accuracy of online image matching, and keypoints may fail to match between images taken at the same location but different seasons. In this paper, we propose a robust loop-closure detection method for robot visual SLAM, which adopts invariant representation as image descriptors composed of learned features and adapts to changes in illumination and seasons. We evaluate our method on real datasets and demonstrate its excellent ability to handle illumination changes.