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An EKF, Accelerometer, Gravity Based Wheel Odometry Method

Gathering information on robot motion is critical in determining its trajectory in an environment. The information about a robot's motion, called odometry, can be collected by tracking the wheels for ground based robots. Wheel encoders are common for monitoring kinematic information of a wheel. Wheel encoders generally split the circumference of the wheel into equally discrete distances to track the distance traveled along the wheel's circumference. The proposal of this research is to dismiss the use of the above discrete based wheel encoders and use an accelerometer as a wheel encoder for a more continuous reading of the wheel's position. Accelerometers are typically difficult to use for precise data collection because of noisy outputs causing inaccurate odometry information. This is overcome by comparing sensor data to modeled behavior and with some filtering. The filter analyzed in this paper is the Extended Kalman Filter and proved to provide accurate wheel tracking with low error, even with disturbances. The maximum error was observed at start up and dropped below 1 % in each environment.