{'fraction_non_alphanumeric': 0.013617021276595745, 'fraction_numerical': 0.005106382978723404, 'mean_word_length': 5.461538461538462, '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}}
A Novel Room Categorization Approach to Semantic Localization for Domestic Service Robots

Recently, room categorization as part of indoor robot localization has become a vital topic for semantic mapping. One approach is implemented via scene understanding by integrating available object information in the scene. In this paper, a novel room association approach is proposed based on the prior knowledge of the object appearance frequency in the specific room category inside the house. The front interface of the proposed technique employs a state-of-the-art YOLOv2-based object detection framework. Detected objects and their prior appearance frequency information form the input to the proposed room association through a novel scoring approach. This scoring function avoids any limit on the number of detected objects and is capable of operating with a low object detection confidence level. The experimental results of the novel proposed technique show significant improvement over the previously developed room categorization approach. On average, the correctness score increased up to 0.8387 while the indecisiveness level of the object detection framework decreases.