{"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/stereo-camera-visual-slam-with-hierarchical","title":"Stereo Camera Visual SLAM with Hierarchical Masking and Motion-state Classification at Outdoor Construction Sites Containing Large Dynamic Objects","arxiv_id":"2101.06563","date":"2021-01-17","proceeding":null,"authors":["Runqiu Bao","Ren Komatsu","Renato Miyagusuku","Masaki Chino","Atsushi Yamashita","Hajime Asama"],"abstract":"At modern construction sites, utilizing GNSS (Global Navigation Satellite System) to measure the real-time location and orientation (i.e. pose) of construction machines and navigate them is very common. However, GNSS is not always available. Replacing GNSS with on-board cameras and visual simultaneous localization and mapping (visual SLAM) to navigate the machines is a cost-effective solution. Nevertheless, at construction sites, multiple construction machines will usually work together and side-by-side, causing large dynamic occlusions in the cameras' view. Standard visual SLAM cannot handle large dynamic occlusions well. In this work, we propose a motion segmentation method to efficiently extract static parts from crowded dynamic scenes to enable robust tracking of camera ego-motion. Our method utilizes semantic information combined with object-level geometric constraints to quickly detect the static parts of the scene. Then, we perform a two-step coarse-to-fine ego-motion tracking with reference to the static parts. This leads to a novel dynamic visual SLAM formation. We test our proposals through a real implementation based on ORB-SLAM2, and datasets we collected from real construction sites. The results show that when standard visual SLAM fails, our method can still retain accurate camera ego-motion tracking in real-time. Comparing to state-of-the-art dynamic visual SLAM methods, ours shows outstanding efficiency and competitive result trajectory accuracy.","url_abs":"https://arxiv.org/abs/2101.06563v1","url_pdf":"https://arxiv.org/pdf/2101.06563v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"stereo-camera-visual-slam-with-hierarchical","repo_url":"https://github.com/RunqiuBao/kenki-positioning-vSLAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}