Position estimation and tracking using optical range data

Abstract

With the use of a scanning optical ranger, a dense map of an environment has been constructed. From this, line segment targets are extracted and matched against an a priori map to obtain observations for an extended Kalman filter. This filter is then able to update predictions of an autonomous guided vehicle's position (made using odometry) in real time, offering high-speed position estimation using inexpensive sensing techniques.