A Multi-Sensor Fusion System for a Laboratory Based Autonomous Vehicle

Motivated by the desire to generate richer descriptions of world state from disparate information sources the research area of Multi Sensor Data Fusion (MSDF) based upon a distributed Kalman Filter is addressed in this paper. To demonstrate the approach the MSDF system is applied i) in simulation to a second order plant and ii) to a laboratory based robot. MSDF research has demonstrated greater accuracy of state estimation which leads to greater system robustness with respect to sensor failure/sensor error. In addition the application of MSDF to systems with zero mean noise processes generates a Kalman filtered state estimate that is less sensitive to poor choices of system and process noise models