Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909605918932992 |
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| author | Kaden, Janik Hilger, Maximilian Schreiter, Tim Schaab, Marius Graichen, Thomas Rudenko, Andrey Heinkel, Ulrich Lilienthal, Achim J. |
| author_facet | Kaden, Janik Hilger, Maximilian Schreiter, Tim Schaab, Marius Graichen, Thomas Rudenko, Andrey Heinkel, Ulrich Lilienthal, Achim J. |
| contents | With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and activity prediction approaches require high quality and quantity of data for training and evaluation, usually collected from motion capture systems, onboard or stationary sensors. Setting up these systems is challenging due to the intricate setup of hardware components, extensive calibration procedures, occlusions, and substantial costs. These constraints make deploying such systems in new and large environments difficult and limit their usability for in-the-wild measurements. In this paper we investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded and occlusion-prone environments. We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison. The environment imitates a museum setup, with up to four active participants navigating toward random goals in a natural way, and offers more than 130 minutes of multi-modal data. Our investigation provides a step toward scalable and accurate motion data collection beyond vision-based systems, laying a foundation for evaluating sensing modalities like UWB in larger and complex environments like warehouses, airports, or convention centers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization Kaden, Janik Hilger, Maximilian Schreiter, Tim Schaab, Marius Graichen, Thomas Rudenko, Andrey Heinkel, Ulrich Lilienthal, Achim J. Robotics Human-Computer Interaction Machine Learning With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and activity prediction approaches require high quality and quantity of data for training and evaluation, usually collected from motion capture systems, onboard or stationary sensors. Setting up these systems is challenging due to the intricate setup of hardware components, extensive calibration procedures, occlusions, and substantial costs. These constraints make deploying such systems in new and large environments difficult and limit their usability for in-the-wild measurements. In this paper we investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded and occlusion-prone environments. We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison. The environment imitates a museum setup, with up to four active participants navigating toward random goals in a natural way, and offers more than 130 minutes of multi-modal data. Our investigation provides a step toward scalable and accurate motion data collection beyond vision-based systems, laying a foundation for evaluating sensing modalities like UWB in larger and complex environments like warehouses, airports, or convention centers. |
| title | Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization |
| topic | Robotics Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2505.05851 |