TOPO-EPFL/CrossLoc-Benchmark-Datasets
[CVPR'22] CrossLoc benchmark datasets setup and helper scripts.
This project provides organized datasets for researchers working on improving the accuracy of aerial drone localization. It takes raw multimodal synthetic data and real drone-captured images with precise geo-tags and prepares them into training, validation, and testing sets, following established conventions. Scientists and engineers focused on computer vision, robotics, or autonomous systems would use this to develop and benchmark new localization algorithms.
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Use this if you are developing or evaluating algorithms for drone localization and need carefully prepared datasets that combine synthetic and real-world aerial imagery with ground truth.
Not ideal if you are looking for a complete, off-the-shelf drone localization solution, as this project focuses solely on providing the data for research.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 22, 2022
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