{'fraction_non_alphanumeric': 0.017101710171017102, 'fraction_numerical': 0.009000900090009001, 'mean_word_length': 5.502923976608187, '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}}
Adaptive Bayesian recognition and pose estimation of 3D industrial objects with optimal feature selection

Visual recognition and pose estimation of 3D industrial objects are challenging due to variations of geometric shapes from perspectives while lacking textures. This paper presents an approach to accurately recognizing industrial objects and estimating their poses based on an adaptive Bayesian framework with optimal feature selection and model-based point cloud matching. First, feature sets that provide sufficient evidences for recognition are selected from a predefined pool of features based on the t-test. Then, the evidence structure for Bayesian framework is updated adaptively to the feature sets selected for recognition. Finally, a process of coarse-fine pose estimation, composed of 3D feature based coarse registration followed by Octree-ICP based model matching for precision registration, is presented. Experiments with automotive and electronic industrial objects show that the proposed method achieves over 97.5% of recognition rate and about 0.5 mm and 0.5 deg of pose estimation error.