Online Variational Bayesian Motion Averaging

In this paper, we propose a novel algorithm dedicated to online motion averaging for large scale problems. To this end, we design a filter that continuously approximates the posterior distribution of the estimated transformations. In order to deal with large scale problems, we associate a variational Bayesian approachwith a relative parametrization of the absolute transformations. Such an association allows our algorithm to simultaneously possess two features that are essential for an algorithm dedicated to large scale online motion averaging: (1) a low computational time, (2) the ability to detect wrong loop closure measurements. We extensively demonstrate on several applications (binocular SLAM, monocular SLAM and video mosaicking) that our approach not only exhibits a low computational time and detects wrong loop closures but also significantly outperforms the state of the art algorithm in terms of RMSE.