Salient Visual Features to Help Close the Loop in 6D SLAM

Abstract

One fundamental problem in mobile robotics research is _Simultaneous Localization and Mapping_ (SLAM): A mobile robot has to localize itself in an unknown environment, and at the same time generate a map of the surrounding area. One fundamental part of SLAM algorithms is loop closing: The robot detects whether it has reached an area that has been visited before, and uses this information to improve the pose estimate in the next step. In this work, visual camera features are used to assist closing the loop in an existing 6 degree of freedom SLAM (6D SLAM) architecture. For our robotics application we propose and evaluate several detection methods, including salient region detection and maximally stable extremal region detection. The detected regions are encoded using SIFT descriptors and stored in a database. Loops are detected by matching of the images' descriptors. A comparison of the different feature detection methods shows that the combination of salient and maximally stable extremal regions suggested by Newman and Ho performs moderately.