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 a downstream task that employs a weighted-average method to estimate the end user’s location. Another downstream task utilizes the perspective-n-point (PnP) algorithm to estimate the end user’s direction by exploiting the 2D–3D point correspondences between the query image and the 3D environment, as extracted from matched images in the database. Additionally, this system implements Dijkstra’s algorithm to calculate a shortest path based on a navigable map that includes the 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 m without knowledge of the camera’s intrinsic parameters, such as focal length.
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Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects
Commercial image search applications like eBay and Pinterest allow users to select the
focused area as bounding box over the query images, which improves the retrieval accuracy. The
focused area image retrieval strategy motivated our research, but our system has three main advantages
over the existing works. (1) Given a query focus area, our approach localizes the most
similar region in the database image and only this region is used for computing image similarity.
This is done in a unified network whose weights are adjusted both for localization and similarity
learning in an end-to-end manner. (2) This is achieved using fewer than five proposals extracted
from a saliency map, which speedups the pairwise similarity computation. Usually hundreds or
even thousands of proposals are used for localization. (3) For users, our system explains the relevance
of the retrieved results by locating the regions in database images most similar to query object.
Our method achieves significantly better retrieval performance than the off-the-shelf object
localization-based retrieval methods and end-to-end trained triplet method with a region proposal
network. Our experimental results demonstrate 86% retrieval rate as compared to 73% achieved
by the existing methods on PASCAL VOC07 and VOC12 datasets. Extensive experiments are also
conducted on the instance retrieval databases Oxford5k and INSTRE, wherewe exhibit competitive
performance. Finally, we provide both quantitative and qualitative results of our retrieval method
demonstrating its superiority over commercial image search systems.
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- Award ID(s):
- 1814745
- NSF-PAR ID:
- 10109410
- Date Published:
- Journal Name:
- Pacific Rim Int. Conf. on Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
