Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation

Place recognition, i.e., the ability to recognize previously seen parts of the environment, is one of the fundamental tasks in mobile robotics. The wide range of applications of place recognition includes localization (determine the initial pose), SLAM (detect loop closures), and change detection in dynamic environments. In the past, only relatively little work has been carried out to attack this problem using 3D range data and the majority of approaches focuses on detecting similar structures without estimating relative poses. In this paper, we present an algorithm based on 3D range data that is able to reliably detect previously seen parts of the environment and at the same time calculates an accurate transformation between the corresponding scan-pairs. Our system uses the estimated transformation to evaluate a candidate and in this way to more robustly reject false positives for place recognition. We present an extensive set of experiments using publicly available datasets in which we compare our system to other state-of-the-art approaches.