{"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/loam-lidar-odometry-and-mapping-in-real-time","title":"LOAM: Lidar Odometry and Mapping in Real-Time","arxiv_id":null,"date":"2014-07-01","proceeding":"Robotics: Science and Systems Conference 2014 7","authors":["Ji Zhang","Sanjiv Singh"],"abstract":"We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause misregistration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements. The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables\r\nsimultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity\r\nof the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point\r\ncloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set\r\nof experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the\r\nlevel of state of the art offline batch methods.","url_abs":"http://www.roboticsproceedings.org/rss10/p07.html","url_pdf":"https://www.ri.cmu.edu/pub_files/2014/7/Ji_LidarMapping_RSS2014_v8.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":"loam-lidar-odometry-and-mapping-in-real-time","repo_url":"https://github.com/anastasiia-kornilova/python-LOAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"simultaneous-localization-and-mapping","task_name":"Simultaneous Localization and Mapping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}