Visual place recognition for aerial imagery: A survey

• URL: http://arxiv.org/abs/2406.00885v2
• Date: Thu, 31 Oct 2024 17:12:57 GMT
• Title: Visual place recognition for aerial imagery: A survey
• Authors: Ivan Moskalenko, Anastasiia Kornilova, Gonzalo Ferrer,
• Abstract summary: Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem. Visual geolocalization is emerging as a viable alternative.
• Score: 3.122408196953972
• License:
• Abstract: Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository -- https://github.com/prime-slam/aero-vloc.

Related papers

• Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition [9.192660643226372]
  We formulate how limitations in the Geographic Distance Sensitivity of current VPR embeddings result in a high probability of incorrectly sorting the top-k retrievals. We propose a novel mining strategy, CliqueMining, that selects positive and negative examples by sampling cliques from a graph of visually similar images. Our approach boosts the sensitivity of VPR embeddings at small distance ranges, significantly improving the state of the art on relevant benchmarks.
  arXiv  Detail & Related papers  (2024-07-02T16:49:01Z)
• Breaking the Frame: Visual Place Recognition by Overlap Prediction [53.17564423756082]
  We propose a novel visual place recognition approach based on overlap prediction, called VOP. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone. Our approach uses a voting mechanism to assess overlap scores for potential database images.
  arXiv  Detail & Related papers  (2024-06-23T20:00:20Z)
• CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition [73.51329037954866]
  We propose a robust global representation method with cross-image correlation awareness for visual place recognition. Our method uses the attention mechanism to correlate multiple images within a batch. Our method outperforms state-of-the-art methods by a large margin with significantly less training time.
  arXiv  Detail & Related papers  (2024-02-29T15:05:11Z)
• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation [15.324623975476348]
  Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. We propose BEV-CV, an approach introducing two key novelties with a focus on improving the real-world viability of cross-view geo-localisation.
  arXiv  Detail & Related papers  (2023-12-23T22:20:45Z)
• Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception? [57.77643186237265]
  We present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets.
  arXiv  Detail & Related papers  (2023-12-07T18:59:14Z)
• A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments [22.58641358408613]
  Visual place recognition (VPR) is an essential component of robot navigation and localization systems. No single VPR technique excels in every environmental condition. adaptive VPR system dubbed Adaptive Multi-Self Identification and Correction (A-MuSIC) A-MuSIC matches or beats state-of-the-art VPR performance across all tested benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-24T19:25:22Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• Cross-Descriptor Visual Localization and Mapping [81.16435356103133]
  Visual localization and mapping is the key technology underlying the majority of Mixed Reality and robotics systems. We present three novel scenarios for localization and mapping which require the continuous update of feature representations. Our data-driven approach is agnostic to the feature descriptor type, has low computational requirements, and scales linearly with the number of description algorithms.
  arXiv  Detail & Related papers  (2020-12-02T18:19:51Z)

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.