  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.
