Online environment mapping using metric-topological maps

Abstract

In this paper we propose a vision-based online mapping of large-scale environments. Our approach uses a hybrid representation of a fully metric Euclidean environment map and a topological map. This novel hybrid representation facilitates our scalable online hierarchical bundle adjustment approach. The proposed method achieves scalability by solving the local registration through embedding neighboring keyframes and landmarks into a Euclidean space. The global adjustment is performed on a segmentation of the keyframes and posed as the iterative optimization of the arrangement of keyframes in each segment and the arrangement of rigidly moving segments. The iterative global adjustment is performed concurrently with the local registration of the keyframes in a local map. Thus, the map is always locally metric around the current location, and likely to be globally consistent. Loop closures are handled very efficiently benefiting from the topological nature of the map and overcoming the loss of the metric map properties of previous approaches. The effectiveness of the proposed method is demonstrated in real-time on various challenging video sequences.