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
Local models and Gaussian mixture models for statistical data processing
Local models and Gaussian mixture models for statistical data processing
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Integration, Coordination and Control of Multi-Sensor Robot Systems
Integration, Coordination and Control of Multi-Sensor Robot Systems
Multisensor Fusion: An Autonomous Mobile Robot
Journal of Intelligent and Robotic Systems
Sensor Selection by GMB-REM in Real Robot Position Estimation
Journal of Intelligent and Robotic Systems
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Building a Local Hybrid Map from Sensor Data Fusion
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Design methodology for context-aware wearable sensor systems
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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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), which introduced the sensor selection technique. 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. A new sensor selection technique incorporated with sensor fusion is introduced in this paper. Actually the new technique is realised by incorporating with the sensor fusion scheme. Empirical results show that the new system outperforms the previous GMB-REM with sensor selection alone. More specifically, we illustrate that the new technique is able to considerably constrain the error of a robot"s position.