A new sensor fusion framework to deal with false detections for low-cost service robot localization

The popularization of service robots requires that robots are not expensive and can work very well in human daily living environment. Due to lighting condition and/or cluttered background, there are false detections occasionally for landmark-based robot localization with low-cost color camera or infrared sensors. However, traditional frequently used methods, such as Extended Kalman Filter (EKF) and Particle Filter (PF), can´t cope with the problem of false detection. A novel sensor fusion framework is proposed in this paper for robot localization, which is capable of dealing with the problem of false detection. It has been tested in a real receptionist robot equipped with an infrared camera and wheel encoders. Experiments illustrate that the proposed method has a better performance than EKF and PF when false detections occur, while maintaining almost same performance during other times.