Pursuing projections: keeping a robot on path

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