JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
| العنوان: | JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments |
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| المؤلفون: | Eric H. Frankel, JunYoung Gwak, Amir Sadeghian, Mihir Patel, Roberto Martín-Martín, Silvio Savarese, Hamid Rezatofighi, Abhijeet Shenoi |
| المصدر: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:6748-6765 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Visual perception, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Point cloud, 02 engineering and technology, Computer Science - Robotics, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Social robot, Audio signal, Mobile manipulator, business.industry, Applied Mathematics, Computational Theory and Mathematics, Robot, RGB color model, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Robotics (cs.RO), Encoder, Software |
| الوصف: | We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360$^\circ$ RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360$^\circ$ spherical image from a fisheye camera and encoder values from the robot's wheels. Our dataset incorporates data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, all from the ego-perspective of the robot, both stationary and navigating. The dataset has been annotated with over 2.3 million bounding boxes spread over 5 individual cameras and 1.8 million associated 3D cuboids around all people in the scenes totaling over 3500 time consistent trajectories. Together with our dataset and the annotations, we launch a benchmark and metrics for 2D and 3D person detection and tracking. With this dataset, which we plan on extending with further types of annotation in the future, we hope to provide a new source of data and a test-bench for research in the areas of egocentric robot vision, autonomous navigation, and all perceptual tasks around social robotics in human environments. |
| تدمد: | 1939-3539 0162-8828 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4f992433785675106089116a50a26f9 https://doi.org/10.1109/tpami.2021.3070543 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f4f992433785675106089116a50a26f9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19393539 01628828 |
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