Reducing the SLAM drift error propagation using sparse but accurate 3D models for augmented reality applications

Abstract

SLAM is the generic name given to the class of methods allowing to incrementally build a 3D representation of an environment while simultaneously using this map to localize a mobile system evolving within this environment. Though quite a mature field, several scientific problems remain open and particularly the reduction of drift. Drift is inherent to SLAM since the task is fundamentally incremental and errors in model estimation are cumulative. In this paper we suggest to take advantage from sparse but accurate knowledge of the environment to periodically reinitialize the system, thus stopping the drift. As it may be of interest in a Augmented reality context, we show this knowledge can be propagated to past estimations through bundle adjustment and present three different strategies to perform this propagation. Experiments carried out in an urban environment are described and demonstrate the efficiency of our approach.