Sensor data fusion using Kalman filter

Abstract

Autonomous robots and vehicles need accurate positioning and localization for their guidance, navigation and control. Often, two or more different sensors are used to obtain reliable data useful for control systems. The paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using an Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The signals used during navigation cannot be always considered as white noise signals. On the other hand, colored signals will cause the EKF to diverge. The AFLS was used to adapt the gain and therefore prevent Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced more accurate signal is used to guide and navigate the robot.