Keyframe detection for appearance-based visual SLAM

This paper is concerned with the problem of keyframe detection in appearance-based visual SLAM. Appearance SLAM models a robot's environment topologically by a graph whose nodes represent strategically interesting places that have been visited by the robot and whose arcs represent spatial connectivity between these places. Specifically, we discuss and compare various methods for identifying the next location that is sufficiently different visually from the previously visited location or node in the map graph in order to decide whether a new node should be created. We survey existing techniques of keyframe detection in image retrieval and video analysis. Using experimental results obtained from visual SLAM datasets, we conclude that the feature matching method offers the best performance among five representative methods in terms of accurately measuring the amount of appearance change between robot's views and thus can serve as a simple and effective metric for detecting keyframes. This study fills an important but missing step in the current appearance SLAM research.