Extended Kalman filter for improved navigation with fault awareness

Most unmanned mobile robotic platforms contain multiple sensors that can be leveraged to measure vehicle motion states, where there often exists redundancies among the different sensor types. Kalman filter based sensor fusion between inertial navigation sensors, GPS readings, encoders, etc. is a very popular approach in the literature to improve the accuracy of navigation readings. However, such redundancies can also be exploited for simultaneously conducting fault detection and identification of the sensors and the robot. This paper presents theory and results for an Extended Kalman Filter (EKF) approach fusing IMU/INS readings with GPS and/or visual odometry (VO) data to diagnose faults in wheel odometry readings (encoders). A key advantage is that the approach works for detecting faults, even when relatively low grade and inexpensive sensors are installed in the vehicle.