CNN-based Joint State Estimation During Robotic Interaction with Articulated Objects
[ 1 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] pracownik
2022
rozdział w monografii naukowej / referat
angielski
EN In this paper, we investigate the problem of state estimation of rotational articulated objects during robotic interaction. We estimate the position of a joint axis and the current rotation of an object from a pair of RGB-D images registered by the depth camera mounted on the robot. However, the camera mounted on the robot has a limited view due to occlusions of the robot's arm. Moreover, some configurations of objects are difficult to register by typical RGB-D sensors. Thus, the model-based methods fail in these cases. To deal with this problem, we propose a CNN-based architecture that gradually estimates the parameters and the state of the rotational joint. To meet real-time requirements on the real robot, we propose a fast inference on 2D images without directly operating on the 3D model of the object. The proposed method is trained and verified on the RBO dataset that contains RGB-D sequences of manipulated articulated objects.
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