Human-Computer Interaction and Human-Building Interaction

An assistive haptic interface for appearance-based indoor navigation

Jose Rivera-Rubio, Kai Arulkumaran, Hemang Rishi, Ioannis Alexiou, Anil A. Bharath

Published 2016

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📄 Paper
Computer vision remains an under-exploited technology for assistive devices. Here, we propose a navigation technique using low-resolution images from wearable or hand-held cameras to identify landmarks that are indicative of a user’s position along crowdsourced paths. We test the components of a system that is able to provide blindfolded users with information about location via tactile feedback. We assess the accuracy of vision-based localisation by making comparisons with estimates of location derived from both a recent SLAM-based algorithm and from indoor surveying equipment. We evaluate the precision and reliability by which location information can be conveyed to human subjects by analysing their ability to infer position from electrostatic feedback in the form of textural (haptic) cues on a tablet device. Finally, we describe a relatively lightweight systems architecture that enables images to be captured and location results to be served back to the haptic device based on journey information from multiple users and devices.

Abstract

Computer vision remains an under-exploited technology for assistive devices. Here, we propose a navigation technique using low-resolution images from wearable or hand-held cameras to identify landmarks that are indicative of a user’s position along crowdsourced paths. We test the components of a system that is able to provide blindfolded users with information about location via tactile feedback. We assess the accuracy of vision-based localisation by making comparisons with estimates of location derived from both a recent SLAM-based algorithm and from indoor surveying equipment. We evaluate the precision and reliability by which location information can be conveyed to human subjects by analysing their ability to infer position from electrostatic feedback in the form of textural (haptic) cues on a tablet device. Finally, we describe a relatively lightweight systems architecture that enables images to be captured and location results to be served back to the haptic device based on journey information from multiple users and devices.

Topics

Case StudyContext-Aware Spatial IntelligenceCross-Modal Intelligence and Multimodal IntegrationHCI-HBIMultimodal Design AssistanceOccupant Behavior Analysis and Spatial OptimizationVision-Language Models for AECXRAI automated accessibilityXRAI for AECXRAI inclusive design

Cite this paper

@article{59dbbeab,
title = {An assistive haptic interface for appearance-based indoor navigation},
journal = {Computer Vision and Image Understanding},
volume = {149},
pages = {126-145},
year = {2016},
note = {Special issue on Assistive Computer Vision and Robotics - "Assistive Solutions for Mobility, Communication and HMI"},
issn = {1077-3142},
doi = {https://doi.org/10.1016/j.cviu.2016.02.014},
url = {https://www.sciencedirect.com/science/article/pii/S1077314216000680},
author = {Jose Rivera-Rubio and Kai Arulkumaran and Hemang Rishi and Ioannis Alexiou and Anil A. Bharath},
keywords = {Human navigation, Assistive technology, Localisation, Mobility, Indoor navigation},
abstract = {Computer vision remains an under-exploited technology for assistive devices. Here, we propose a navigation technique using low-resolution images from wearable or hand-held cameras to identify landmarks that are indicative of a user’s position along crowdsourced paths. We test the components of a system that is able to provide blindfolded users with information about location via tactile feedback. We assess the accuracy of vision-based localisation by making comparisons with estimates of location derived from both a recent SLAM-based algorithm and from indoor surveying equipment. We evaluate the precision and reliability by which location information can be conveyed to human subjects by analysing their ability to infer position from electrostatic feedback in the form of textural (haptic) cues on a tablet device. Finally, we describe a relatively lightweight systems architecture that enables images to be captured and location results to be served back to the haptic device based on journey information from multiple users and devices.}
}