Map-based localization using the panoramic horizon

Presents an approach to solve the localization problem, in which an observer is given a topographic map of an area and dropped off at an unknown location. The solution to this problem requires establishing correspondences between viewer-centered observable features and their location on the map. The feature the authors select is the panoramic horizon curve, defined as the sky-ground boundary perceived by the observer as he performs a full 360/spl deg/ in place. In the authors' approach, they first precompute, offline, these horizon curves at a set of locations on a grid, from the topological map. These curves are approximated by polygons with different line fitting tolerances to gain robustness to noise in the authors' representation. These polygons are grouped into overlapping super segments, which are then encoded and stored in a table. The online computation consists of acquiring the panoramic view and extracting (with human help) the horizon curve. This curve is approximated by a polygon and the resulting super segments, used as indices in the data base, allow one to retrieve candidate locations. The best candidate is selected during a verification step which applies geometric constraints. This process uses local features and can therefore tolerate significant occlusion likely to occur in real environments. The authors illustrate the performance of the approach on results obtained from real data.