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RoPose: CNN-based 2D pose estimation of industrial robots

  • As production workspaces become more mobile and dynamic it becomes increasingly important to reliably monitor the overall state of the environment. Therein manipulators or other robotic systems likely have to be able to act autonomously together with humans and other systems within a joint workspace. Such interactions require that all components in non-stationary environments are able to perceive the state relative to each other. As vision-sensors provide a rich source of information to accomplish this, we present RoPose, a convolutional neural network (CNN) based approach, to estimate the two dimensional joint configuration of a simulated industrial manipulator from a camera image. This pose information can further be used by a novel targetless calibration setup to estimate the pose of the camera relative to the manipulator’s space. We present a pipeline to automatically generate synthetic training data and conclude with a discussion of the potential usage of the same pipeline to acquire real image datasets of physically existent robots.

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Author of HS ReutlingenGulde, Thomas; Ludl, Dennis; Curio, Cristóbal
Erschienen in:2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) : Munich, Germany, August 20-24, 2018
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Year of Publication:2018
Page Number:8
First Page:463
Last Page:470
DDC classes:004 Informatik
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE