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ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization

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arxiv 2207.12317 v1 pith:JFROIXZY submitted 2022-07-19 cs.CV cs.RO

Ivan Cisneros , Peng Yin , Ji Zhang , Howie Choset , Sebastian Scherer This is my paper
classification cs.CVcs.RO
keywords datasetaltolocalizationvisualbenchmarkinghighimageryplace
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We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes high precision GPS-INS ground truth location data, high precision accelerometer readings, laser altimeter readings, and RGB downward facing camera imagery. In addition, we provide reference imagery over the flight paths, which makes this dataset suitable for VPR benchmarking and other tasks common in Localization, such as image registration and visual odometry. To the author's knowledge, this is the largest real-world aerial-vehicle dataset of this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Visual Place Recognition for Large-Scale UAV Applications

    cs.CV 2025-07 conditional novelty 7.0 of 10

    A million-image aerial place recognition dataset from Estonia, plus a demonstration that steerable (rotation-equivariant) CNNs give robust gains over standard ResNet baselines in aerial visual place recognition.

  2. UAVScenes: A Multi-Modal Dataset for UAVs

    cs.CV 2025-07 conditional novelty 6.0 of 10

    UAVScenes adds frame-wise image and LiDAR semantic labels, reconstructed 6-DoF poses, and 3D maps to 120k frames of the MARS-LVIG dataset, with six benchmark tasks.

  3. AeroMap3D: Anchoring Monocular UAV 6-DoF Localization to Visual-Geometric-Semantic Map Priors

    cs.RO 2026-07 conditional novelty 5.0 of 10

    AeroMap3D combines a scale/yaw adapter, a frozen dense matcher, OSM-filtered DEM lifting, and an EKF to achieve 5.88 m mean 3D localization error over 55 km of UAV flight using only public maps.

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