Efficient vision-based loop closing techniques for delayed state robot mapping

Abstract

This paper shows results on outdoor vision-based loop closing for Simultaneous Localization and Mapping. Our experiments show that for loops of over 50m, the pose estimates maintained with a Delayed-State Extended Information Filter are consistent enough to guarantee assertion of vision-based pose constraints for loop closure, provided no necessary information links are added to the estimator. The technique computes relative pose constraints via a robust least squares minimisation of 3D point correspondences, which are in turn obtained from the matching of SIFT features over candidate image pairs. We propose a loop closure test that checks both for closeness of means and for highly informative updates at the same time.