Literature DB >> 33263114

An Indoor Navigation App using Computer Vision and Sign Recognition.

Giovanni Fusco1, Seyed Ali Cheraghi1, Leo Neat2, James M Coughlan1.   

Abstract

Indoor navigation is a major challenge for people with visual impairments, who often lack access to visual cues such as informational signs, landmarks and structural features that people with normal vision rely on for wayfinding. Building on our recent work on a computer vision-based localization approach that runs in real time on a smartphone, we describe an accessible wayfinding iOS app we have created that provides turn-by-turn directions to a desired destination. The localization approach combines dead reckoning obtained using visual-inertial odometry (VIO) with information about the user's location in the environment from informational sign detections and map constraints. We explain how we estimate the user's distance from Exit signs appearing in the image, describe new improvements in the sign detection and range estimation algorithms, and outline our algorithm for determining appropriate turn-by-turn directions.

Entities:  

Keywords:  Accessibility; Blindness; Low Vision; Navigation; Visual Impairment; Wayfinding

Year:  2020        PMID: 33263114      PMCID: PMC7703403          DOI: 10.1007/978-3-030-58796-3_56

Source DB:  PubMed          Journal:  Comput Help People Spec Needs


  3 in total

1.  INSIGHT: RFID and Bluetooth enabled automated space for the blind and visually impaired.

Authors:  Aura Ganz; Siddhesh Rajan Gandhi; Carole Wilson; Gary Mullett
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

2.  Indoor Localization using Computer Vision and Visual-Inertial Odometry.

Authors:  Giovanni Fusco; James M Coughlan
Journal:  Comput Help People Spec Needs       Date:  2018-06-26

3.  Indoor Localization for Visually Impaired Travelers Using Computer Vision on a Smartphone.

Authors:  Giovanni Fusco; James M Coughlan
Journal:  Proc 17th Int Web All Conf (2020)       Date:  2020-04
  3 in total
  1 in total

1.  Real-Time Sign Detection for Accessible Indoor Navigation.

Authors:  Seyed Ali Cheraghi; Giovanni Fusco; James M Coughlan
Journal:  J Technol Pers Disabil       Date:  2021
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.