Sensor based robot localisation and navigation: using interval analysis and extended Kalman filter

This paper describes a new approach for mobile robot navigation using an interval analysis based adaptive mechanism for an extended Kalman filter. The robot is equipped with inertial sensors, encoders and ultrasonic sensors. The map used for this study is two-dimensional and it is assumed to be a known a-priori. Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. An extended Kalman filter (EKF) is used to estimate the robots position using the inertial sensors and encoders. Since the EKF estimates are affected by bias, drift etc. we propose an adaptive mechanism using interval analysis to correct these defects in estimates. Interval analysis has been already successfully used in the past for robot localisation using time of flight ultrasonic sensors. One of the problems of the use of interval analysis sensor based navigation and localisation is that it can be applicable only in the presence of landmarks. This problem is overcome here using additional sensors such as encoders and inertial sensors, which gives an estimate of the robot position using an extended Kalman filter in the absence of landmarks. In the presence of landmarks the complementary robot position information from the interval analysis algorithm using ultrasonic sensors is used to estimate and bound the errors in the EKF robot position estimate.