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

Xia Yuan¹, Kaiyang Wang², Riyu Qin²

¹Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, 210094, China. yuanxia@njust.edu.cn.

Scientific Data

|May 7, 2025

Summary

This study introduces a new multimodal dataset for precise robot localization, fusing LiDAR, aerial, and ground-view data. It enables robust navigation in challenging environments where Global Navigation Satellite Systems (GNSS) fail.

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Area of Science:

Robotics and Autonomous Systems
Geospatial Information Science
Computer Vision

Background:

High-precision localization is essential for intelligent robots in various fields, but Global Navigation Satellite System (GNSS) performance degrades in obstructed environments.
Existing datasets for cross-view pose estimation lack real-world field scenarios and high-resolution Light Detection and Ranging (LiDAR) data.

Purpose of the Study:

To introduce a novel multimodal dataset for cross-view pose estimation, addressing limitations in current resources.
To facilitate the development of robust localization systems for robots operating in GNSS-denied conditions.

Main Methods:

Collected a synchronized dataset of 29,940 frames featuring 144-channel LiDAR point clouds, ground-view RGB images, and aerial orthophotos across 11 diverse environments.
Ensured centimeter-level georeferencing accuracy using GNSS fusion and post-processed kinematic positioning.
Integrated high-resolution LiDAR, aerial, and ground-view data triplets for comprehensive evaluation.

Main Results:

The dataset uniquely combines field and urban scenarios with high-fidelity sensor data.
Provides a valuable resource for evaluating 3-DoF pose estimation algorithms, focusing on orientation alignment and coordinate transformation.
Offers LiDAR-enhanced ground truth for advanced outdoor navigation research.

Conclusions:

The developed multimodal dataset significantly advances the evaluation of cross-view localization techniques.
It supports the creation of more reliable and accurate navigation systems for intelligent robots in challenging, real-world conditions.
This resource is crucial for research in multisensor fusion and feature matching for robotic localization.