Combining intelligent techniques for sensor fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.