Robust Map Alignment for Cooperative Visual SLAM

Abstract

Generating a map of an unknown environment using visual SLAM with sparse image features is an important task in robotics and other computer vision applications. As the number of available recordings increases, merging maps from multiple sources into an aggregated description of the environment becomes necessary. After identifying similar locations, an important step is the estimation of the transformation that aligns the maps. The aim of this work is to evaluate different methods for computing this transformation and to provide a novel way of estimating the scale difference between maps using a histogram-based scale matching scheme. The proposed approach proves to be more robust than the currently widely used scale estimation methods for loop closure or map merging.