{'fraction_non_alphanumeric': 0.01880674448767834, 'fraction_numerical': 0.0012970168612191958, 'mean_word_length': 5.594017094017094, '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}}
IGR report Robust , Adaptive Visual Navigation using Multiple DiverseVisual Cues

Robust Computer Vision is becoming increasingly important as an enabling technology in a broad range of applications which include industrial automation, industrial inspection, medical diagnosis, human-computer interaction, surveillance, document processing, video database management and virtual reality. Although, our primary motivation is aimed at robotic visual navigation, the ability of computer vision systems to employ many diverse visual cues will be relevant to many of these application domains, allowing decision making processes within these applications to be maximally informed [4]. It is the fundamental aim of this research to attack robustness and adaptability issues within the domain of mobile robot visual navigation. This is a challenging application, since, as the vehicle moves around, the various visual cues that aid navigation disappear and reappear in the robot’s visible environment. The main objective is to develop new computer vision algorithms which employ multiple visual cues to reliably segment the ground plane in the robot’s field of view [3]. The assumption is that the robot is equipped with a single uncalibrated camera and that many scenes will contain features which are mostly coplanar so that structure and motion methods based on estimation of fundamental matrices cannot be used. The algorithms should work in any indoor scenario, irrespective of feature content, provided the floor is flat to some approximation.