{'fraction_non_alphanumeric': 0.01639344262295082, 'fraction_numerical': 0.0, 'mean_word_length': 5.622641509433962, '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}}
Variable Resolution Occupancy Mapping Using Gaussian Mixture Models

Occupancy mapping is fundamental for active perception systems to enable reasoning about known and unknown regions of the environment. The majority of occupancy mapping approaches enforce an a priori discretization on the environment, resulting in a fixed resolution map that limits the expressiveness of the representation. The proposed approach removes this a priori discretization, learns continuous representations for the evidence of occupied and free space to derive the probability of occupancy, and enables occupancy grid maps to be generated at arbitrary resolution. Efficient methods are also presented that accurately evaluate the probability of occupancy in individual cells and enable multi-resolution mapping and local occupancy evaluation. The efficacy of the approach is demonstrated by comparison to state-of-the-art discrete and continuous mapping techniques in both two dimensions and three dimensions. The core contribution of this work is a memory-efficient method for deriving occupancy that is amenable to small or large corrections in pose without the need to regenerate the entire map. The applications under considerations are low-bandwidth scenarios (e.g., multi-robot exploration) and operations in expansive environments, where storing an occupancy grid map of the entire environment would be prohibitive.