{'fraction_non_alphanumeric': 0.015332197614991482, 'fraction_numerical': 0.0017035775127768314, 'mean_word_length': 4.994897959183674, '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}}
Robust and fast 3D object recognition using exhaustive search and ICP algorithm

This paper proposes a robust and fast alignment method of 3D point cloud. The problem of point cloud alignment can be roughly classified into two. The problem of estimation of the relation from unknown geometry position is coarse registration. Moreover, in case of roughly position is known, the problem of estimation of the relation from known initial position is fine registration. In this study, we solve the coarse registration problem by exhaustive search, and prepare the initial positions for fine registration. To use exhaustive search, we realize a robust. The fine registration problem is solved by the ICP algorithm. In this case, reduction of initial positions for the ICP algorithm by non-extremum suppression that uses distance between range data and model solves the problem of calculation cost. The distance evaluation function that robust for measurement error tackles the outlier problem. The problem of calculation cost is solved by registering the distance from the model beforehand by the distance field. The effectiveness of the proposed method is shown by a actual data.