In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. We propose using a recursive total least squares algorithm to obtain estimates of the robot position. We avoid several weaknesses inherent in the use of the Kalman and extended Kalman filters, achieving much faster convergence without good initial (a priori) estimates of the position. The performance of the method is illustrated both by simulation and on an actual mobile robot with a camera.

For an autonomous robot navigating in an unstructured outdoor environment, staying close to a path is crucial to successfully reaching its goal. Although the degree of accuracy with which it estimates its own location affects its ability to stay on the path, accuracy in estimate of lateral distance from the path is far more important for successful navigation than accuracy in estimate of position along the path. Utilizing methods based only on relative angular measurements between landmarks in the environment, we draw from techniques used in statistical pattern recognition to show how landmarks can be chosen for localization which will not only give good estimate of location in spite of the measurement error, but will also keep the robot on the path. We demonstrate how identical landmark configurations can produce very different results in localizing to a path and show how simple heuristics can be used to choose the best configuration for path localization.>

A robot navigating in an unstructured outdoor environment must determine its own location in spite of problems due to environmental conditions, sensor limitations and map inaccuracies, exact measurements are seldom known, and the combination of approximate measures can lead to large errors in self-localization. The conventional approach to this problem has been to deal with the errors either during processing or after they occur. The authors maintain that it is possible to limit the errors before they occur. The authors analyze how measurement errors affect errors in localization and propose that a simple algorithm can be used to exploit the geometric properties of landmarks in the environment in order to decrease errors in localization. The authors' goal is to choose landmarks that will provide the best localization regardless of measurement error, determine the best areas in which to identify new landmarks to be used for further localization and choose paths that will provide the least chance of "straying". The authors show the result of implementing this concept in experiments run in simulation with USGS 30 m DEM data for a robot statically locating, following a path and identifying new landmarks.>