Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor fusion in certainty grids for mobile robots
Sensor devices and systems for robotics
The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Improved Sensor Selection Technique by Integrating Sensor Fusion in Robot Position Estimation
Journal of Intelligent and Robotic Systems
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Modelling and reducing uncertainty are two essential problems with mobile robot localisation. Previously we developed a robot localisation system, namely, the Gaussian Mixture of Bayes with Regularised Expectation Maximisation (GMB-REM), using a single sensor. GMB-REM allows a robot"s position to be modelled as a probability distribution, and uses Bayes" theorem to reduce the uncertainty of its location. In this paper, a new system for performing sensor selection is introduced, namely an enhanced form of GMB-REM. Empirical results show the new system outperforms GMB-REM using sonar alone. More specifically, it is able to select between multiple sensors at each robot"s position, and further minimises the average robot localisation error.