Robust mobile robot localization using optical flow sensors and encoders

Abstract

Open-loop estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity of an estimate. However, these methods can sometimes lead to inaccurate or unreliable positional estimates. Using one or more optical flow sensors, a method has been developed which can accurately track position in both ideal kinematic conditions and otherwise. Using optical flow techniques and available sensors, reliable positional estimates are made. The sensor provides accurate measurement of the movement at the sensor location. Even though the sensor does not provide angular displacement, the robot movement is estimated using only one sensor even with wheel slip. However, when the robot moves sideways due to external disturbance, redundant sensors are used in order to estimate the configuration of the robot. Pseudo inverse based estimation and the extended Kalman filter based estimation are presented to show the effectiveness of the proposed approach. Location of the sensors has also been investigated in order to minimize errors caused by inaccurate sensor readings. Finally, the method is implemented and tested using a potential field based navigation scheme.