{'fraction_non_alphanumeric': 0.01893095768374165, 'fraction_numerical': 0.0, 'mean_word_length': 5.033557046979865, '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}}
Using Vision for Outdoor-slam

In this paper we outline how we seek to augment existing mapping technology with visual competencies designed to enable Simultaneous Localization and Mapping (SLAM) in outdoor settings. We briefly describe the notion of visual similarity between scenes and discuss how it can be applied to scene labelling, loop closure and the definition of distinctive places. However, the main questions are, how topological constraints can improve state-of-the-art SLAM techniques for outdoor environments, which role play metric measurements in large scale mapping, and what is an appropriate description for such maps. Due to the complexity of those problems, we do not yet seek to present full answers or solutions. The intention of this paper is rather to present and discuss our ideas and research objectives in the context of related previous work both our own and external.