{'fraction_non_alphanumeric': 0.02164009111617312, 'fraction_numerical': 0.010250569476082005, 'mean_word_length': 4.9391891891891895, '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}}
Global Place Recognition using An Improved Scan Context for LIDAR-based Localization System

Global place recognition is a crucial ability for unmanned ground vehicles since it is the premise of localization in a prior map. However, global place recognition using only 3D LIDAR is still a challenging problem. In this paper, a LIDAR-based place recognition method within a 3D Point cloud map is proposed. The global descriptor Scan Context (SC) is adopted and improved by building a local reference frame so that it’s more robust to viewpoint changes. Accordingly, a three-stage matching algorithm is proposed to efficiently perform place recognition. The proposed method is verified on the KITTI dataset. The experimental results show that the proposed method can perform precise place recognition in about 200ms and the success rate is around 98%, which is 16% higher than SC.