Improved Sensor Selection Technique by Integrating Sensor Fusion in Robot Position Estimation

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.