Development of a Sensor Fusion System for Control and Navigation of Mobile Vehicle.

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In this research, a development of the sensor fusion algorithm for control of a mobile vehicle is investigated. The output data from the visual sensor include a time-lag due to the image processing computation. The sampling rate of the visual sensor is considerably low so that it should be used with other sensors to control fast motion. The development of an algorithm to integrate the data from multi-sensor system to obtain the optimal estimate of the state variables is an important problem for control and navigation of mobile vehicle. The main purpose of this research is to develop a method which constitutes a sensor fusion system to give the optimal state estimates for control and navigation of mobile vehicle. The proposed sensor fusion system combines a vision sensor and a inertial sensor using a modified Kalman filter. A kind of multi-rate Kalman filter which treats the slow sampling rate data from the vision sensor is applied for the construction of the sensor fusion system. An experimental mobile robot is developed which realizes a sensor fusion system combining a vision sensor system and an inertial sensor system. Experimental results show that the proposed sensor fusion algorithm gives the precise estimates of the state variables combining the data from CCD camera images and the inertial sensors.