학술논문
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
Document Type
Periodical
Author
Source
IEEE Robotics and Automation Letters IEEE Robot. Autom. Lett. Robotics and Automation Letters, IEEE. 8(5):2788-2795 May, 2023
Subject
Language
ISSN
2377-3766
2377-3774
2377-3774
Abstract
This letter introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects, which is a common case in imitation learning, where the object is typically partially occluded by the manipulating hand. Currently, there is a lack of datasets that would enable the development of robust 6D pose estimation methods for these conditions. To overcome this problem, we collect a new dataset (Imitrob) aimed at 6D pose estimation in imitation learning and other applications where a human holds a tool and performs a task. The dataset contains image sequences of nine different tools and twelve manipulation tasks with two camera viewpoints, four human subjects, and left/right hand. Each image is accompanied by an accurate ground truth measurement of the 6D object pose obtained by the HTC Vive motion tracking device. The use of the dataset is demonstrated by training and evaluating a recent 6D object pose estimation method (DOPE) in various setups.