Skip to Main Content
In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based, visual localization concept with a Rao-Blackwellized Particle Filter (RBPF) as state estimator to a real-world suitable, online SLAM approach. In our system, each RBPF particle incrementally constructs its own graph-based environment model which is labeled with visual appearance features (extracted from panoramic 360deg snapshots of the environment) and the estimated poses of the places where the snapshots were captured. The essential advantages of this appearance-based SLAM approach are its low memory and computing-time requirements. Therefore, the algorithm is able to perform in real-time. Finally, we present the results of SLAM experiments in two challenging environments that investigate the stability and localization accuracy of this SLAM technique.