{'fraction_non_alphanumeric': 0.022885572139303482, 'fraction_numerical': 0.005970149253731343, 'mean_word_length': 4.952662721893491, '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}}
Object pose estimation for robotic control with 3D range data

Object pose estimation is an important application in three-dimensional (3-D) object recognition. A 3-D geometrical feature descriptor, Angle Distance Map (ADM), is proposed to describe the surface feature around a local point in range images. A triangular mesh model of the 3-D object is used for reducing the computational complexity. The principal component analysis (PCA) method is applied on the ADM descriptions of surface mesh vertices. The projected vectors of an ADM description in subeigenspace are then used to match the feature points and determine the object pose. The matched feature points are refined by a proposed outlier elimination approach. The pose information is computed according to the final 3-D feature points. The proposed approach is tested in an application for flexible robot assembly. The experimental results show that accurate pose estimation can be obtained using the ADM descriptions on 3-D object surfaces.