Ground based sensing systems for autonomous agricultural vehicles
This paper examines ground based (as opposed to satellite based) sensing methods for vehicle position fixing. Sensors are considered in various categories, motion measurement (odometry, inertial), artificial landmarks (laser positioning, millimetre wave radar), and local feature detection (sonar, machine vision). Particular emphasis is paid to technologies which have proven successful beyond the field of agriculture, and to machine vision because of its topicality. The importance of sensor fusion, using a sound theoretical framework, is emphasised. The most common technique, the Kalman filter, is outlined and practical points are discussed. As an example system, the autonomous vehicle developed at Silsoe Research Institute is described. This vehicle does not use an absolute positioning system, rather it navigates using local features, in this case the crop plants. This vehicle uses a sensor package that includes machine vision, odometers, accelerometers, and a compass, where sensor fusion is accomplished using an extended Kalman filter.
sensors kalman filter autonomous vehicles navigation localisation data fusion