{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/locnet-global-localization-in-3d-point-clouds","title":"LocNet: Global localization in 3D point clouds for mobile vehicles","arxiv_id":"1712.02165","date":"2017-12-06","proceeding":null,"authors":["Huan Yin","Li Tang","Xiaqing Ding","Yue Wang","Rong Xiong"],"abstract":"Global localization in 3D point clouds is a challenging problem of estimating\nthe pose of vehicles without any prior knowledge. In this paper, a solution to\nthis problem is presented by achieving place recognition and metric pose\nestimation in the global prior map. Specifically, we present a semi-handcrafted\nrepresentation learning method for LiDAR point clouds using siamese LocNets,\nwhich states the place recognition problem to a similarity modeling problem.\nWith the final learned representations by LocNet, a global localization\nframework with range-only observations is proposed. To demonstrate the\nperformance and effectiveness of our global localization system, KITTI dataset\nis employed for comparison with other algorithms, and also on our long-time\nmulti-session datasets for evaluation. The result shows that our system can\nachieve high accuracy.","url_abs":"http://arxiv.org/abs/1712.02165v2","url_pdf":"http://arxiv.org/pdf/1712.02165v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"locnet-global-localization-in-3d-point-clouds","repo_url":"https://github.com/ZJUYH/LocNet_caffe","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.02165","atlas_url":"https://app.syntology.ai/?focus=1712.02165","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}