Evaluation of Image Matching Methods for Visual Odometry on UAVs

• URL: http://arxiv.org/abs/2608.18624v1
• Date: Wed, 19 Aug 2026 07:17:48 GMT
• Title: Evaluation of Image Matching Methods for Visual Odometry on UAVs
• Abstract summary: Unmanned aerial vehicles (UAVs) are a powerful tool for many environmental monitoring and transport applications.<n>Their reliance on Global Navigation Satellite System (GNSS) technology for navigation makes them susceptible to catastrophic failures.<n>This work explores Visual Odometry (VO) as a crucial navigation component.
• Score: 0.9558392439655014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicles (UAVs) are becoming a powerful tool for many environmental monitoring and transport applications. Yet, their reliance on Global Navigation Satellite System (GNSS) technology for navigation makes them susceptible to catastrophic failures in scenarios where the positioning signal is unavailable or disrupted. This work explores Visual Odometry (VO) as a crucial navigation component. Recently, numerous deep-learning-based methods for image matching have been proposed that are yet to be implemented in a fully-fledged VO system. In this paper, we evaluate recent state-of-the-art image matching methods for the task of VO for UAV position tracking, with a downwards-facing camera, on our synthetic dataset, and find that while the best results are generated by the recent RoMa matcher, SIFT features can outperform some recent state-of-the-art.

Related papers

• AUSLUN: A Fixed-Hover UAV--USV System for GNSS-Denied Maritime Search and Navigation [7.220023631779745]
  Global navigation satellite system (GNSS) denial can prevent an unmanned surface vehicle (USV) from both finding a distant vessel and maintaining a globally referenced approach.<n>This paper presents AUSLUN, a fixed-hover aerial-surface system that uses a coastal unmanned aerial vehicle (UAV) as a long-range sensing and navigation anchor.<n>Results establish the feasibility and operating boundary of fixed-hover UAV assistance for stationary-target approach in coastal-denied environments.
  arXiv  Detail & Related papers  (2026-06-29T07:15:59Z)
• Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception [51.687842983240564]
  Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) remains challenging due to the severe geometric discrepancy between oblique UAV imagery and satellite maps.<n>We propose a geometry-aware UAV geo-localization framework that explicitly models the 3D scene geometry to unify coarse place recognition and fine-grained pose estimation.<n>Our method significantly outperforms state-of-the-art baselines, achieving robust meter-level localization accuracy and improved generalization in complex urban environments.
  arXiv  Detail & Related papers  (2026-04-02T08:08:41Z)
• Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation [51.286599397552756]
  We present Bearing-UAV, a vision-driven cross-view navigation method that jointly predicts UAV absolute location and heading from neighboring features.<n>We also present Bearing-UAV-90k, a benchmark for evaluating cross-view localization and navigation.
  arXiv  Detail & Related papers  (2026-03-23T16:17:39Z)
• A Deep Learning Framework with Geographic Information Adaptive Loss for Remote Sensing Images based UAV Self-Positioning [10.16507150219648]
  Self-positioning of UAVs in GPS-denied environments has become a critical objective.<n>We present a deep learning framework with geographic information adaptive loss to achieve precise localization.<n>Results demonstrate the method's efficacy in enabling UAVs to achieve precise self-positioning.
  arXiv  Detail & Related papers  (2025-02-22T09:36:34Z)
• Leveraging edge detection and neural networks for better UAV localization [5.781342655426309]
  We propose a novel method for geolocalizing Unmanned Aerial Vehicles (UAVs) in environments lacking Global Navigation Satellite Systems (GNSS) Current state-of-the-art techniques employ an offline-trained encoder to generate a vector representation (embedding) of the UAV's current view. We demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges.
  arXiv  Detail & Related papers  (2024-04-09T10:56:46Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions [2.8983738640808645]
  This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
  arXiv  Detail & Related papers  (2023-08-08T14:25:13Z)
• Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments [20.69412701553767]
  Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. This paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task.
  arXiv  Detail & Related papers  (2022-01-23T07:18:55Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

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.