A simple map-based localization strategy using range measurements

In this paper we present a map-based approach to localization. We consider indoor navigation in known environments based on the idea of a "vector cloud" by observing that any point in a building has an associated vector defining its distance to the key structural components (e.g., walls, ceilings, etc.) of the building in any direction. Given a building blueprint we can derive the "ideal" vector cloud at any point in space. Then, given measurements from sensors on the robot we can compare the measured vector cloud to the possible vector clouds cataloged from the blueprint, thus determining location. We present algorithms for implementing this approach to localization, using the Hamming norm. the 1-norm. and the 2-norm. The effectiveness of the approach is verified by experiments on a 2-D testbed using a mobile robot with a 360 degrees laser range-finder and through simulation analysis of robustness.