Planning Routes through uncertain territory
Artificial Intelligence
Communications of the ACM
Representing and acquiring geographic knowledge
Representing and acquiring geographic knowledge
On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Integration, Coordination and Control of Multi-Sensor Robot Systems
Integration, Coordination and Control of Multi-Sensor Robot Systems
Coping with Uncertainty in Map Learning
Coping with Uncertainty in Map Learning
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Some theoretical aspects of position-location problems
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Random walks, universal traversal sequences, and the complexity of maze problems
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Robot introspection through learned hidden Markov models
Artificial Intelligence
Learning dynamics: system identification for perceptually challenged agents
Artificial Intelligence
Coping with uncertainty in a control system for navigation and exploration
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Inferring finite automata with stochastic output functions and an application to map learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate two approaches to learning maps under such conditions: one based on Valiant's probably approximately correct learning model, and a second based on Rivest Sz Sloan's reliable and probably nearly almost always useful learning model. Both methods deal with the problem of accumulated error in combining local measurements to make global inferences. In the first approach, the effects of accumulated error are eliminated by the use of reliable and probably useful methods for discerning the local properties of space. In the second, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. Finally, we suggest some insights into why certain existing techniques for map learning perform as well as they do.