UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision

Anbang Yang; Mahya Beheshti; Todd E Hudson; Rajesh Vedanthan; Wachara Riewpaiboon; Pattanasak Mongkolwat; Chen Feng; John-Ross Rizzo

doi:10.3390/s22228894

arXiv · 2209.11336

UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision

Anbang Yang·Mahya Beheshti·Todd E Hudson·Rajesh Vedanthan·Wachara Riewpaiboon·Pattanasak Mongkolwat·Chen Feng·John-Ross Rizzo

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Abstract

Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible at scale. Herein, we propose a novel vision-based localization pipeline for a specific use case: navigation support for end-users with blindness and low vision. Given a query image taken by an end-user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space. The geolocations of these similar images are utilized in downstream tasks that employ a weighted-average method to estimate the end-user's location and a perspective-n-point (PnP) algorithm to estimate the end-user's direction. Additionally, this system implements Dijkstra's algorithm to calculate a shortest path based on a navigable map that includes trip origin and destination. The topometric map used for localization and navigation is built using a customized graphical user interface that projects a 3D reconstructed sparse map, built from a sequence of images, to the corresponding a priori 2D floor plan. Sequential images used for map construction can be collected in a pre-mapping step or scavenged through public databases/citizen science. The end-to-end system can be installed on any internet-accessible device with a camera that hosts a custom mobile application. For evaluation purposes, mapping and localization were tested in a complex hospital environment. The evaluation results demonstrate that our system can achieve localization with an average error of less than 1 meter without knowledge of the camera's intrinsic parameters, such as focal length.

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BibTeX RIS

Anbang Yang, Mahya Beheshti, Todd E Hudson, Rajesh Vedanthan, Wachara Riewpaiboon, Pattanasak Mongkolwat, Chen Feng, John-Ross Rizzo. 2022-09-22. UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision. https://doi.org/10.3390/s22228894

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