Hierarchical localization using entropy-based feature map and triangulation techniques

Hierarchical localization provides both topological and quantitative metric solutions, with faster performance for the latter since the searchable space is minimized. The initial topological localization step is crucial in those frameworks and should be highly accurate. In this paper, a hierarchical localization approach that primarily focuses on the efficiency of the topological module is presented. The approach relies on a minimal set of qualitative entropy-based local features, which achieves both speed and localization accuracy. The abundant features are triangulated in a next step using a photogrammetric projective model to obtain a metric solution. The metric localization selects only the correct matches by regarding a simple yet efficient distance measure to overcome problems of data association and environment dynamics.