A multi-modality ground-to-air cross-view pose estimation dataset for field robots

High-precision localization is critical for intelligent robotics in autonomous driving, smart agriculture, and military operations. While Global Navigation Satellite System (GNSS) provides global positioning, its reliability deteriorates severely in signal degraded environments like urban canyons. C...

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Bibliographic Details
Published in:Scientific data Vol. 12; no. 1; pp. 754 - 15
Main Authors: Yuan, Xia, Wang, Kaiyang, Qin, Riyu, Xu, Jiachen
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 07.05.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/987
639/705/258
Algorithms
Coordinate transformations
Data Descriptor
Datasets
Humanities and Social Sciences
Lidar
Localization
multidisciplinary
Robots
Science
Science (multidisciplinary)
ISSN:2052-4463, 2052-4463
Online Access:Get full text
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Summary:High-precision localization is critical for intelligent robotics in autonomous driving, smart agriculture, and military operations. While Global Navigation Satellite System (GNSS) provides global positioning, its reliability deteriorates severely in signal degraded environments like urban canyons. Cross-view pose estimation using aerial-ground sensor fusion offers an economical alternative, yet current datasets lack field scenarios and high-resolution LiDAR support.This work introduces a multimodal cross-view dataset addressing these gaps. It contains 29,940 synchronized frames across 11 operational environments (6 field environments, 5 urban roads), featuring: 1) 144-channel LiDAR point clouds, 2) ground-view RGB images, and 3) aerial orthophotos. Centimeter-accurate georeferencing is ensured through GNSS fusion and post-processed kinematic positioning. The dataset uniquely integrates field environments and high-resolution LiDAR-aerial-ground data triplets, enabling rigorous evaluation of 3-DoF pose estimation algorithms for orientation alignment and coordinate transformation between perspectives.This resource supports development of robust localization systems for field robots in GNSS-denied conditions, emphasizing cross-view feature matching and multisensor fusion. Light Detection And Ranging (LiDAR)-enhanced ground truth further distinguishes its utility for complex outdoor navigation research.
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ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-025-05075-9