{"id":166984,"date":"2014-09-01T00:00:00","date_gmt":"2014-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/travi-navi-self-deployable-indoor-navigation-system\/"},"modified":"2018-10-16T21:24:26","modified_gmt":"2018-10-17T04:24:26","slug":"travi-navi-self-deployable-indoor-navigation-system","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/travi-navi-self-deployable-indoor-navigation-system\/","title":{"rendered":"Travi-Navi: Self-deployable Indoor Navigation System"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present Travi-Navi \u2013 a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider\u2019s walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and<br \/>\ndetect deviation events within 9 steps.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present Travi-Navi \u2013 a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider\u2019s walk on the navigation paths, collects a rich set of sensor [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"liqul"},{"type":"user_nicename","value":"chunzhao"},{"type":"user_nicename","value":"zhao"},{"type":"user_nicename","value":"jackysh"}],"msr_publishername":"ACM - Association for Computing Machinery","msr_publisher_other":"","msr_booktitle":"Mobicom'14","msr_chapter":"","msr_edition":"Mobicom'14","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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