{'fraction_non_alphanumeric': 0.01935483870967742, 'fraction_numerical': 0.0018433179723502304, 'mean_word_length': 5.135593220338983, '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}}
Application of Simultaneous Localization and Mapping for Large-scale Manipulators in Unknown Environments

In this paper, we study the application of simultaneous localization and mapping (SLAM) for estimating the tool center point (TCP) 6 degrees-of-freedom (DOF) pose of a largescale hydraulic manipulator without a priori knowledge of the environment. We attach a stereo camera near the TCP of the manipulator and perform SLAM by utilizing the open source version of ORB-SLAM2. In offline experiments, the camera frame and the TCP frame are extrinsically calibrated using an iterative closest point search to match a point cloud of poses from the SLAM module with a point cloud of ground-truth TCP poses, which are obtained from joint encoder measurements along with a kinematic model of the manipulator. The estimated TCP trajectory provided by the SLAM is then compared to the ground-truth TCP trajectory. These preliminary experiments show that a pure visual SLAM algorithm can perform reasonably well in this application scenario. Limitations and future work are also discussed.