{"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/navigationnet-a-large-scale-interactive","title":"NavigationNet: A Large-scale Interactive Indoor Navigation Dataset","arxiv_id":"1808.08374","date":"2018-08-25","proceeding":null,"authors":["He Huang","Yujing Shen","Jiankai Sun","Cewu Lu"],"abstract":"Indoor navigation aims at performing navigation within buildings. In scenes\nlike home and factory, most intelligent mobile devices require an functionality\nof routing to guide itself precisely through indoor scenes to complete various\ntasks in order to serve human. In most scenarios, we expected an intelligent\ndevice capable of navigating itself in unseen environment. Although several\nsolutions have been proposed to deal with this issue, they usually require\npre-installed beacons or a map pre-built with SLAM, which means that they are\nnot capable of working in novel environments. To address this, we proposed\nNavigationNet, a computer vision dataset and benchmark to allow the utilization\nof deep reinforcement learning on scene-understanding-based indoor navigation.\nWe also proposed and formalized several typical indoor routing problems that\nare suitable for deep reinforcement learning.","url_abs":"http://arxiv.org/abs/1808.08374v1","url_pdf":"http://arxiv.org/pdf/1808.08374v1.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":[],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[{"slug":"navigationnet","name":"NavigationNet","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}