An adaptive algorithm for position and linear velocity estimation of mobile robots using inertial sensors and omnidirectional vision

This paper presents a novel and effective method for estimating the position and linear velocity of a mobile robot in real-time in unknown environments by fusing visual information from an omnidirectional vision system with the measurements of an inertial sensor system. Based on a new projection model of the omnidirectional vision system, we propose a novel adaptive algorithm, similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the position and linear velocity of the robot on-line using continuously tracked natural feature points in image sequence, the robot´s acceleration, angular velocity and orientation measured by inertial sensor system. It is proven that our algorithm leads to global exponential convergence of the estimation errors of the robot´s position and linear velocity to zero. Experiments demonstrate the performance of the proposed algorithm.