{"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/rgbid-slam-for-accurate-real-time","title":"RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping","arxiv_id":"1807.08271","date":"2018-07-22","proceeding":null,"authors":["Daniel Gutierrez-Gomez","Jose J. Guerrero"],"abstract":"In this paper we present a complete SLAM system for RGB-D cameras, namely\nRGB-iD SLAM. The presented approach is a dense direct SLAM method with the main\ncharacteristic of working with the depth maps in inverse depth parametrisation\nfor the routines of dense alignment or keyframe fusion. The system consists in\n2 CPU threads working in parallel, which share the use of the GPU for dense\nalignment and keyframe fusion routines. The first thread is a front-end\noperating at frame rate, which processes every incoming frame from the RGB-D\nsensor to compute the incremental odometry and integrate it in a keyframe which\nis changed periodically following a covisibility-based strategy. The second\nthread is a back-end which receives keyframes from the front-end. This thread\nis in charge of segmenting the keyframes based on their structure, describing\nthem using Bags of Words, trying to find potential loop closures with previous\nkeyframes, and in such case perform pose-graph optimisation for trajectory\ncorrection. In addition, our system allows is able to compute the odometry both\nwith unregistered and registered depth maps, allowing to use customised\ncalibrations of the RGB-D sensor. As a consequence in the paper we also propose\na detailed calibration pipeline to compute customised calibrations for\nparticular RGB-D cameras. The experiments with our approach in the TUM RGB-D\nbenchmark datasets show results superior in accuracy to the state-of-the-art in\nmany of the sequences. The code has been made available on-line for research\npurposes https://github.com/dangut/RGBiD-SLAM.","url_abs":"http://arxiv.org/abs/1807.08271v1","url_pdf":"http://arxiv.org/pdf/1807.08271v1.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":"rgbid-slam-for-accurate-real-time","repo_url":"https://github.com/dangut/RGBiD-SLAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"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}