In the robot navigation problem, noisy sensor data. must be filtered to obtain the best estimate of the 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. The use of the total least squares filter is proposed which is capable of converging with many fewer readings and achieving greater accuracy than the classical Kalman filter. In this paper, a complete survey with regards to direct and iterative methods based on our recent research work is described to solve the total least squares problems. This filter solved by iterative methods is very promising for very large data information and from our experiments we can obtain more precise accuracy.