Sensor data fusion using Unscented Kalman Filter for accurate localization of mobile robots

Abstract

This paper presents a sensor-data-fusion method using an Unscented Kalman Filter (UKF), to implement an accurate localization system for mobile robots. Integration of data from various sensors using an efficient sensor fusion algorithm is required to achieve a continuous and accurate localization of mobile robots. We use data from low cost accelerometer, gyroscope, and encoders to obtain robot motion information. The UKF, used as an efficient sensor fusion algorithm, is an advanced filtering technique which outperforms the widely-used Extended Kalman Filter (EKF) in many applications. The system is able to compensate for the slip errors by switching between two different UKF models built for slip and no-slip cases. Since the accelerometer error accumulates with time because of the double integration, the data from accelerometer is only used in slip model of the UKF. The results obtained from UKF sensor fusion algorithm are compared with the results from an accurate distance laser sensor. The experimental results show that the system is able to accurately track the motion of the robot in various robot motion scenarios including the scenario when robot's encoders data is not reliable due to the slip occurrence.