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Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation.
Rodziewicz-Bielewicz, Jan; Korzen, Marcin.
Afiliación
  • Rodziewicz-Bielewicz J; Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Zolnierska 49, 71-210 Szczecin, Poland.
  • Korzen M; Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Zolnierska 49, 71-210 Szczecin, Poland.
Sensors (Basel) ; 22(17)2022 Aug 29.
Article en En | MEDLINE | ID: mdl-36080976
The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. The tracking system consists of two parts: image preprocessing and machine learning. In the machine learning part, we compare two approaches: fitting the robot pose to the point cloud and fitting the convolutional neural network model to the sparse 3D depth images. The advantage of the presented approach is direct use of the point cloud transformed to the sparse image in the network input and use of sparse convolutional and pooling layers (sparse CNN). The experiments confirm that the robot tracking is performed in real time and with an accuracy comparable to the accuracy of the depth sensor.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Robótica / Aprendizaje Profundo Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Polonia Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Robótica / Aprendizaje Profundo Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Polonia Pais de publicación: Suiza