Fault tolerant Kalman filter architecture for mobile robot localization.

Summary Accurate localization is an important functionality in autonomous robots and intelligent vehicles. It uses different sensors to determine a position, which is fundamental for navigation and control. In this paper, we propose a fault-tolerant architecture suitable for data fusion and the details of its application for localization of a mobile robot. We use two types of sensors to perceive the state of the robot and the environment: an inertial measurement unit (IMU) that gives the accelerations and angular velocities of the robot, and a camera that provides image sequences for a visual odometry algorithm. A Kalman filter uses these inputs to estimate the robot's position. Fault tolerance is provided in this application by a duplication / comparison of appropriate diagnostic algorithms. The fault injection technique is used to evaluate the performance of our architecture on a simulated case study.