{"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/distributed-mapping-with-privacy-and","title":"Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models","arxiv_id":"1702.03435","date":"2017-02-11","proceeding":null,"authors":["Siddharth Choudhary","Luca Carlone","Carlos Nieto","John Rogers","Henrik I. Christensen","Frank Dellaert"],"abstract":"We consider the following problem: a team of robots is deployed in an unknown\nenvironment and it has to collaboratively build a map of the area without a\nreliable infrastructure for communication. The backbone for modern mapping\ntechniques is pose graph optimization, which estimates the trajectory of the\nrobots, from which the map can be easily built. The first contribution of this\npaper is a set of distributed algorithms for pose graph optimization: rather\nthan sending all sensor data to a remote sensor fusion server, the robots\nexchange very partial and noisy information to reach an agreement on the pose\ngraph configuration. Our approach can be considered as a distributed\nimplementation of the two-stage approach of Carlone et al., where we use the\nSuccessive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as\nworkhorses to split the computation among the robots. As a second contribution,\nwe extend %and demonstrate the applicability of the proposed distributed\nalgorithms to work with object-based map models. The use of object-based models\navoids the exchange of raw sensor measurements (e.g., point clouds) further\nreducing the communication burden. Our third contribution is an extensive\nexperimental evaluation of the proposed techniques, including tests in\nrealistic Gazebo simulations and field experiments in a military test facility.\nAbundant experimental evidence suggests that one of the proposed algorithms\n(the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS\nrequires minimal information exchange, has an anytime flavor, scales well to\nlarge teams, is robust to noise, and is easy to implement. Our field tests show\nthat the combined use of our distributed algorithms and object-based models\nreduces the communication requirements by several orders of magnitude and\nenables distributed mapping with large teams of robots in real-world problems.","url_abs":"http://arxiv.org/abs/1702.03435v1","url_pdf":"http://arxiv.org/pdf/1702.03435v1.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":"distributed-mapping-with-privacy-and","repo_url":"https://github.com/CogRob/distributed-mapper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}