{'fraction_non_alphanumeric': 0.018378378378378378, 'fraction_numerical': 0.0, 'mean_word_length': 5.475524475524476, '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}}
Light condition invariant visual SLAM via entropy based image fusion

Cameras have always been a popular type of sensor in simultaneous localization and mapping (SLAM) applications. Images, however, often suffer from light and environment changes. One typical limitation is image saturation; sudden change in lighting conditions causes saturation in the image and prohibits the algorithm from producing meaningful measurements. In this paper, we introduce a stereo vision system with different exposure values. Unlike other image processing approaches, we aim to achieve instant saturation detection and fusion with no delay. When the lighting condition change occurs, we detect the over or under-exposed regions using entropy-based metrics and generate a restored image. The proposed method has been validated using an indoor office environment and outdoor parking lot in which the lighting condition is drastically changed.