Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Robust Real-Time Face Detection
International Journal of Computer Vision
Robotics and Autonomous Systems
Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM
International Journal of Robotics Research
Learning to close loops from range data
International Journal of Robotics Research
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Despite significant developments in the Simultaneous Localisation and Mapping (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 classifier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and significant changes in rotation and translation. We developed 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 specification of thresholds given the features.