Robust feature matching for loop closing and localization
Recently, many vision-based SLAM methods have achieved good results using visual features. However, most algorithms suffer from the accumulated error that inevitably occurs. In this paper, we propose a robust loop detection method by matching image features between the incoming image and key-frame images saved in SLAM. Loop detection is a task of deciding whether a robot has returned to a previously visited area or not. Because a camera is unlikely to have the same pose when a robot revisits the place where it previously encountered, it is crucial to match the features under the different views of the scene. In contrast with view-invariant features, it is hard to match corner points in that situation due to the large variation of neighboring pixels. So we present the robust corner matching method under the view changes. Experimental results demonstrate the capability of the loop closing and mobile robot localization under the different views using the proposed method.