Position estimation and tracking using optical range data

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