||Pirker Katrin, Rüther Matthias, Bischof Horst
||Proc. IEEE/RSJ Conference on Intelligent Robots and Systems (IROS) (to appear)
||When performing large-scale perpetual localization and mapping one faces problems like memory consumption or repetitive and dynamic scene elements requiring robust data association. We propose a visual SLAM method which handles short- and long-term scene dynamics in large environments using a single camera only. Through visibility-dependent map filtering and efficient keyframe organization we reach a considerable performance gain only through incorporation of a slightly more complex map representation. Experiments on a large, mixed indoor/outdoor dataset over a time period of two weeks demonstrate the scalability and robustness of our approach.