City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent

• URL: http://arxiv.org/abs/2203.05612v1
• Date: Thu, 10 Mar 2022 19:54:12 GMT
• Title: City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent
• Authors: Lena M. Downes, Dong-Ki Kim, Ted J. Steiner and Jonathan P. How
• Abstract summary: Cross-view image geolocalization provides an estimate of an agent's global position by matching a local ground image to an overhead satellite image without the need for GPS. Our approach, called Wide-Area Geolocalization (WAG), combines a neural network with a particle filter to achieve global position estimates for agents moving in GPS-denied environments. WAG achieves position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach.
• Score: 38.140216125792755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view image geolocalization provides an estimate of an agent's global position by matching a local ground image to an overhead satellite image without the need for GPS. It is challenging to reliably match a ground image to the correct satellite image since the images have significant viewpoint differences. Existing works have demonstrated localization in constrained scenarios over small areas but have not demonstrated wider-scale localization. Our approach, called Wide-Area Geolocalization (WAG), combines a neural network with a particle filter to achieve global position estimates for agents moving in GPS-denied environments, scaling efficiently to city-scale regions. WAG introduces a trinomial loss function for a Siamese network to robustly match non-centered image pairs and thus enables the generation of a smaller satellite image database by coarsely discretizing the search area. A modified particle filter weighting scheme is also presented to improve localization accuracy and convergence. Taken together, WAG's network training and particle filter weighting approach achieves city-scale position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach. Applied to a smaller-scale testing area, WAG reduces the final position estimation error by 64% compared to a state-of-the-art baseline from the literature. WAG's search space discretization additionally significantly reduces storage and processing requirements.

Related papers

• Weakly-supervised Camera Localization by Ground-to-satellite Image Registration [52.54992898069471]
  We propose a weakly supervised learning strategy for ground-to-satellite image registration. It derives positive and negative satellite images for each ground image. We also propose a self-supervision strategy for cross-view image relative rotation estimation.
  arXiv  Detail & Related papers  (2024-09-10T12:57:16Z)
• Adapting Fine-Grained Cross-View Localization to Areas without Fine Ground Truth [56.565405280314884]
  This paper focuses on improving the performance of a trained model in a new target area by leveraging only the target-area images without fine GT. We propose a weakly supervised learning approach based on knowledge self-distillation. Our approach is validated using two recent state-of-the-art models on two benchmarks.
  arXiv  Detail & Related papers  (2024-06-01T15:58:35Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• Cross-View Visual Geo-Localization for Outdoor Augmented Reality [11.214903134756888]
  We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-28T01:58:03Z)
• Wide-Area Geolocalization with a Limited Field of View Camera [33.34809839268686]
  Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching images taken from a ground-view camera to overhead images taken from satellites or aircraft. ReWAG is a neural network and particle filter system that is able to globally localize a mobile agent in a GPS-denied environment with only odometry and a 90 degree FOV camera.
  arXiv  Detail & Related papers  (2022-09-23T20:59:26Z)
• Visual Cross-View Metric Localization with Dense Uncertainty Estimates [11.76638109321532]
  This work addresses visual cross-view metric localization for outdoor robotics. Given a ground-level color image and a satellite patch that contains the local surroundings, the task is to identify the location of the ground camera within the satellite patch. We devise a novel network architecture with denser satellite descriptors, similarity matching at the bottleneck, and a dense spatial distribution as output to capture multi-modal localization ambiguities.
  arXiv  Detail & Related papers  (2022-08-17T20:12:23Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.