{"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/navigating-the-landscape-for-real-time","title":"Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality","arxiv_id":"1808.06352","date":"2018-08-20","proceeding":null,"authors":["Sajad Saeedi","Bruno Bodin","Harry Wagstaff","Andy Nisbet","Luigi Nardi","John Mawer","Nicolas Melot","Oscar Palomar","Emanuele Vespa","Tom Spink","Cosmin Gorgovan","Andrew Webb","James Clarkson","Erik Tomusk","Thomas Debrunner","Kuba Kaszyk","Pablo Gonzalez-de-Aledo","Andrey Rodchenko","Graham Riley","Christos Kotselidis","Björn Franke","Michael F. P. O'Boyle","Andrew J. Davison","Paul H. J. Kelly","Mikel Luján","Steve Furber"],"abstract":"Visual understanding of 3D environments in real-time, at low power, is a huge\ncomputational challenge. Often referred to as SLAM (Simultaneous Localisation\nand Mapping), it is central to applications spanning domestic and industrial\nrobotics, autonomous vehicles, virtual and augmented reality. This paper\ndescribes the results of a major research effort to assemble the algorithms,\narchitectures, tools, and systems software needed to enable delivery of SLAM,\nby supporting applications specialists in selecting and configuring the\nappropriate algorithm and the appropriate hardware, and compilation pathway, to\nmeet their performance, accuracy, and energy consumption goals. The major\ncontributions we present are (1) tools and methodology for systematic\nquantitative evaluation of SLAM algorithms, (2) automated,\nmachine-learning-guided exploration of the algorithmic and implementation\ndesign space with respect to multiple objectives, (3) end-to-end simulation\ntools to enable optimisation of heterogeneous, accelerated architectures for\nthe specific algorithmic requirements of the various SLAM algorithmic\napproaches, and (4) tools for delivering, where appropriate, accelerated,\nadaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.","url_abs":"http://arxiv.org/abs/1808.06352v1","url_pdf":"http://arxiv.org/pdf/1808.06352v1.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":"navigating-the-landscape-for-real-time","repo_url":"https://github.com/dannofield/Particle-Filter-Kidnapped-Vehicle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"navigating-the-landscape-for-real-time","repo_url":"https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P6-Kidnapped-Vehicle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}