Learning Topological SLAM using Visual Information

摘要

The map building from data collected form the environment is an important field in robotics. The map could be used in different tasks, as localization, place recognition, obstacle avoidance, SLAM, etc. A topological map does not seek accurate measures, but the classification of the real environment in different areas. The use of learning techniques can help us to define areas which the robot is able to recognize in subsequent steps. In this paper, we propose the adaptation of the Viola-Jones supervised learning method based on AdaBoost to learn what visual features are good to classify an image into a given area. In our case, AdaBoost will select the best MSER features that best define each node of the map.