Iterative total least squares filter in robot navigation

In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of robot position. The discrete Kalman filter, which usually is used for prediction and detection of signals in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data. However, due to the special domain of robot navigation, the Kalman approach is very limited. Here we propose the use of an iterative total least squares filter which is solved by applying the Lanczos bidiagonalization process. This filter is very promising for very large amounts of data and from our experiments we can obtain a more precise accuracy than with the Kalman filter