CroCoDL: Cross-device Collaborative Dataset for Localization
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Date
2025
Publication Type
Conference Paper
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yes
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Abstract
Accurate localization plays a pivotal role in the autonomy of systems operating in unfamiliar environments, particularly when interaction with humans is expected. High-accuracy visual localization systems encompass various components, such as image retrievers, feature extractors, matchers, reconstruction and pose estimation methods. This complexity translates to the necessity of robust evaluation settings and pipelines. However, existing datasets and benchmarks primarily focus on single-agent scenarios, overlooking the critical issue of cross-device localization. Different agents with different sensors will show their own specific strengths and weaknesses, and the data they have available varies substantially. This work addresses this gap by enhancing an existing augmented reality visual localization benchmark with data from legged robots, and evaluating human-robot, cross-device mapping and localization. Our contributions extend beyond device diversity and include high environment variability, spanning ten distinct locations ranging from disaster sites to art exhibitions. Each scene in our dataset features recordings from robot agents, hand-held and head-mounted devices, and high-accuracy ground truth LiDAR scanners, resulting in a comprehensive multi-agent dataset and benchmark. This work represents a significant advancement in the field of visual localization benchmarking, with key insights into the performance of cross-device localization methods across diverse settings.
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published
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Book title
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Volume
Pages / Article No.
27424 - 27434
Publisher
IEEE
Event
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025)
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Subject
Visual localization; Structure-from-motion; Local features; Cross-device; Benchmark; 3D reconstruction; Augmented reality; Human-robot interaction; Image matching; Image retrieval
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc