Map building and localization are two crucial abilities that autonomous robots must develop. Vision sensors have become a widespread option to solve these problems. When using this kind of sensors, the robot must extract the necessary information from the scenes to build a representation of the environment where it has to move and to estimate its position and orientation with robustness. The techniques based on the global appearance of the scenes constitute one of the possible approaches to extract this information. They consist in representing each scene using only one descriptor which gathers global information from the scene. These techniques present some advantages comparing to other classical descriptors, based on the extraction of local features. However, it is important a good configuration of the parameters to reach a compromise between computational cost and accuracy. In this paper we make an exhaustive comparison among some global appearance descriptors to solve the mapping and localization problem. With this aim, we make use of several image sets captured in indoor environments under realistic working conditions. The datasets have been collected using an omnidirectional vision sensor mounted on the robot.

In this work, a framework is proposed to build topological models in mobile robotics, using an omnidirectional vision sensor as the only source of information. The model is structured hierarchically into three layers, from one high-level layer which permits a coarse estimation of the robot position to one low-level layer to refine this estimation efficiently. The algorithm is based on the use of clustering approaches to obtain compact topological models in the high-level layers, combined with global appearance techniques to represent robustly the omnidirectional scenes. Compared to the classical approaches based on the extraction and description of local features, global-appearance descriptors lead to models that can be interpreted and handled more intuitively. However, while local-feature techniques have been extensively studied in the literature, global-appearance ones require to be evaluated in detail to test their efficacy in map-building tasks. The proposed algorithms are tested with a set of publicly available panoramic images captured in realistic environments. The results show that global-appearance descriptors along with some specific clustering algorithms constitute a robust alternative to create a hierarchical representation of the environment.

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global‐appearance techniques applied to wide‐angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map‐building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.