Learning to Detect Loop Closure from Range Data

Despite signicant developments in the Simultaneous Localisation and Map- ping (slam) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and signicant changes in rotation and translation. We devel- oped a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching slam in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specication of thresholds given the features