Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Experiences with an interactive museum tour-guide robot
Artificial Intelligence - Special issue on applications of artificial intelligence
Exploring artificial intelligence in the new millennium
Trajectory Optimization using Reinforcement Learning for Map Exploration
International Journal of Robotics Research
Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach
Robotics and Autonomous Systems
Bayesian calibration for Monte Carlo localization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Dynamic motion modelling for legged robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Modeling mobile robot motion with polar representations
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A hybrid approach to RBPF based SLAM with grid mapping enhanced by line matching
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Machine learning methods are often applied to the problem of learning a map from a robot's sensor data, but they are rarely applied to the problem of learning a robot's motion model. The motion model, which can be influenced by robot idiosyncrasies and terrain properties, is a crucial aspect of current algorithms for Simultaneous Localization and Mapping (SLAM). In this paper we concentrate on generating the correct motion model for a robot by applying EM methods in conjunction with a current SLAM algorithm. In contrast to previous calibration approaches, we not only estimate the mean of the motion, but also the interdependencies between motion terms, and the variances in these terms. This can be used to provide a more focused proposal distribution to a particle filter used in a SLAM algorithm, which can reduce the resources needed for localization while decreasing the chance of losing track of the robot's position. We validate this approach by recovering a good motion model despite initialization with a poor one. Further experiments validate the generality of the learned model in similar circumstances.