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 robotic dataset%2C for training and evaluating their dynamic scene understanding algorithms. Specifically%2C the THUD dataset construction is first detailed%2C including organization%2C acquisition%2C and annotation methods. It comprises both real-world and synthetic data%2C collected with a real robot platform and a physical simulation platform%2C respectively. Our current dataset includes 13 larges-scale dynamic scenarios%2C 90K image frames%2C 20M 2D%2F3D bounding boxes of static and dynamic objects%2C camera poses%2C and IMU. The dataset is still continuously expanding. Then%2C the performance of mainstream indoor scene understanding tasks%2C e.g. 3D object detection%2C semantic segmentation%2C and robot relocalization%2C is evaluated on our THUD dataset. These experiments reveal serious challenges for some robot scene understanding tasks in dynamic scenes. By sharing this dataset%2C we aim to foster and iterate new mobile robot algorithms quickly for robot actual working dynamic environment%2C i.e. complex crowded dynamic scenes.?quality=80&w=800)
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