Topological visual mapping in robotics.
A key problem in robotics is the construction of a map from its environment. This map could be used in different tasks, like localization, recognition, obstacle avoidance, etc. Besides, the simultaneous location and mapping (SLAM) problem has had a lot of interest in the robotics community. This paper presents a new method for visual mapping, using topological instead of metric information. For that purpose, we propose prior image segmentation into regions in order to group the extracted invariant features in a graph so that each graph defines a single region of the image. Although others methods have been proposed for visual SLAM, our method is complete, in the sense that it makes all the process: it presents a new method for image matching; it defines a way to build the topological map; and it also defines a matching criterion for loop-closing. The matching process will take into account visual features and their structure using the graph transformation matching (GTM) algorithm, which allows us to process the matching and to remove out the outliers. Then, using this image comparison method, we propose an algorithm for constructing topological maps. During the experimentation phase, we will test the robustness of the method and its ability constructing topological maps. We have also introduced new hysteresis behavior in order to solve some problems found building the graph.
Department of Computer Science and Artificial Intelligence, University of Alicante, P.O. Box 99, 03080, Alicante, Spain. aromero@dccia.ua.es
Anna
A
Romero
Miguel
M
Cazorla
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008377:Maps as Topic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics