Global localization in a dense continuous topological map

Vision-based topological maps for mobile robot localization traditionally consist of a set of images captured along a path, with a query image then compared to every individual map image. This paper introduces a new approach to topological mapping, whereby the map consists of a set of landmarks that are detected across multiple images, spanning the continuous space between nodal images. Matches are then made to landmarks, rather than to individual images, enabling a topological map of far greater density than traditionally possible, without sacrificing computational speed. Furthermore, by treating each landmark independently, a probabilistic approach to localization can be employed by taking into account the learned discriminative properties of each landmark. An optimization stage is then used to adjust the map according to speed and localization accuracy requirements. Results for global localization show a greater positive location identification rate compared to the traditional topological map, together with enabling a greater localization resolution in the denser topological map, without requiring a decrease in frame rate.