Planning the Motions of a Mobile Robot in a Sensory Uncertainty Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Toward selecting and recognizing natural landmarks
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning to select useful landmarks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor
IEICE - Transactions on Information and Systems
Robust matching area selection for terrain matching using level set method
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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We describe techniques to optimally select landmarks for performing mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. We use a sensor error model to estimate a probability distribution over the terrain expected to be seen from the current robot position. The estimated distribution is compared to a previously generated map of the terrain and the optimal landmark is selected by minimizing the predicted uncertainty in the localization. This approach has been applied to the generation of a sensor uncertainty field that can be used to plan a robot's movements. Experiments indicate that landmark selection improves not only the localization uncertainty, but also the likelihood of success. Examples of landmark selection are given using real and synthetic data.