{'fraction_non_alphanumeric': 0.013417521704814523, 'fraction_numerical': 0.0, 'mean_word_length': 5.038095238095238, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Simulation of the visual self-localization of mobile robots based on image similarity metrics

In the paper the idea of the visual self-localization of mobile robots based on the estimation of image similarity is discussed. It is assumed that a rough position of the mobile robot is known e.g. from the built-in GPS device. Assuming known orientation of the robot, the main advantage of the application of image analysis algorithms is the increase of the self-localization accuracy. The basic idea of our approach is related to the use of image similarity metrics for the estimation of the distance to the known positions associated with the images stored in the database. In order to verify the the validity of the proposed approach for the images containing textures characteristic for urban areas, some experiments have been conducted in Java based Simbad environment. As the result of applying various image similarity metrics for this purpose, the noticeable increase of the localization accuracy has been obtained for the artificial model of the urban area developed in Simbad environment. Nevertheless, achieved results are encouraging for further experiments utilizing natural images captured by real cameras also in the presence of various image distortions.