A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
A Set Theoretic Approach to Dynamic Robot Localization and Mapping
Autonomous Robots
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
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This article deals with uncertainty and imprecision treatment during the mobile robot localization process. The imprecision determination is based on the use of the interval formalism. Indeed, the mobile robot is equipped with an exteroceptive sensor and odometers. The imprecise data given by these two sensors are fused by constraint propagation on intervals. At the end of the algorithm, we get 3D localization subpaving which is supposed to contain the robot's position in a guaranteed way. Concerning the uncertainty, it is managed through a propagation architecture based on the use of the Transferable Belief Model of Smets. This architecture enables to propagate uncertainty from low level data (sensor data) in order to quantify the global uncertainty of the robot localization estimation.