Computer vision will drive the next wave of robot applications. Latest three-dimensional scanners provide increasingly realistic object reconstructions. We introduce an innovative simulator that allows interacting with those scanners within the operating environment, thus creating a powerful tool for developers, researchers and students. In particular, we present a novel approach for simulating structured-light and time-of-flight sensors. Qualitative results demonstrate the efficiency and reliability in industrial environments. By using the programmability of modern GPUs, it is now possible to make greater use of parallelized simulative approaches. Apart from the easy modification of sensor parameters, the main advantage in simulation is the opportunity of carrying out experiments under reproducible conditions, especially for dynamic scene setups. Moreover, thanks to a great computational power, it is possible to generate huge amounts of synthetic data which can be used as test datasets for training machine learning models.

Vostok: 3D scanner simulation for industrial robot environments

Barbiero M.;Carli R.;
2020

Abstract

Computer vision will drive the next wave of robot applications. Latest three-dimensional scanners provide increasingly realistic object reconstructions. We introduce an innovative simulator that allows interacting with those scanners within the operating environment, thus creating a powerful tool for developers, researchers and students. In particular, we present a novel approach for simulating structured-light and time-of-flight sensors. Qualitative results demonstrate the efficiency and reliability in industrial environments. By using the programmability of modern GPUs, it is now possible to make greater use of parallelized simulative approaches. Apart from the easy modification of sensor parameters, the main advantage in simulation is the opportunity of carrying out experiments under reproducible conditions, especially for dynamic scene setups. Moreover, thanks to a great computational power, it is possible to generate huge amounts of synthetic data which can be used as test datasets for training machine learning models.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3393067
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