Travi-Navi: self-deployable indoor navigation system

We present Travi-Navi - 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's 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 detect deviation events within 9 steps.

[1]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[2]  Pei Zhang,et al.  SugarTrail: Indoor navigation in retail environments without surveys and maps , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[3]  Guobin Shen,et al.  Walkie-Markie: Indoor Pathway Mapping Made Easy , 2013, NSDI.

[4]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[5]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[6]  Dieter Fox,et al.  KLD-Sampling: Adaptive Particle Filters , 2001, NIPS.

[7]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[9]  Song Han,et al.  WheelLoc: Enabling continuous location service on mobile phone for outdoor scenarios , 2013, 2013 Proceedings IEEE INFOCOM.

[10]  Mahadev Satyanarayanan,et al.  Scalable crowd-sourcing of video from mobile devices , 2013, MobiSys '13.

[11]  Nicholas A. Giudice,et al.  Indoor magnetic navigation for the blind , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Chandramohan A. Thekkath,et al.  StarTrack: a framework for enabling track-based applications , 2009, MobiSys '09.

[13]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[14]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[15]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[16]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[17]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[18]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[19]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[20]  Romit Roy Choudhury,et al.  Did you see Bob?: human localization using mobile phones , 2010, MobiCom.

[21]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[22]  SalvadorStan,et al.  Toward accurate dynamic time warping in linear time and space , 2007 .

[23]  William G. Griswold,et al.  ActiveCampus: experiments in community-oriented ubiquitous computing , 2004, Computer.

[24]  Paramvir Bahl,et al.  Energy characterization and optimization of image sensing toward continuous mobile vision , 2013, MobiSys '13.

[25]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[26]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[27]  Chuck Rieger,et al.  PinPoint: An Asynchronous Time-Based Location Determination System , 2006, MobiSys '06.

[28]  Justin Manweiler,et al.  FOCUS: clustering crowdsourced videos by line-of-sight , 2013, SenSys '13.

[29]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[30]  Chuan Qin,et al.  TagSense: a smartphone-based approach to automatic image tagging , 2011, MobiSys '11.

[31]  Mo Li,et al.  Use it free: instantly knowing your phone attitude , 2014, MobiCom.

[32]  Ig-Jae Kim,et al.  Indoor location sensing using geo-magnetism , 2011, MobiSys '11.

[33]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[34]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[35]  Justin Manweiler,et al.  Satellites in our pockets: an object positioning system using smartphones , 2012, MobiSys '12.