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Integrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation

1
Department of Computer Science, Jaume I University, 12071 Castellon, Spain
2
Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Gabriel Oliver-Codina
Sensors 2021, 21(4), 1437; https://doi.org/10.3390/s21041437
Received: 21 December 2020 / Revised: 8 February 2021 / Accepted: 11 February 2021 / Published: 19 February 2021
(This article belongs to the Section Sensors and Robotics)
Deep learning is the mainstream paradigm in computer vision and machine learning, but performance is usually not as good as expected when used for applications in robot vision. The problem is that robot sensing is inherently active, and often, relevant data is scarce for many application domains. This calls for novel deep learning approaches that can offer a good performance at a lower data consumption cost. We address here monocular depth estimation in warehouse automation with new methods and three different deep architectures. Our results suggest that the incorporation of sensor models and prior knowledge relative to robotic active vision, can consistently improve the results and learning performance from fewer than usual training samples, as compared to standard data-driven deep learning. View Full-Text
Keywords: deep learning in sensing; robot sensors; vision/camera based sensors; 3D sensing; monocular depth estimation; warehouse automation; optic flow deep learning in sensing; robot sensors; vision/camera based sensors; 3D sensing; monocular depth estimation; warehouse automation; optic flow
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MDPI and ACS Style

Yoneyama, R.; Duran, A.J.; del Pobil, A.P. Integrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation. Sensors 2021, 21, 1437. https://doi.org/10.3390/s21041437

AMA Style

Yoneyama R, Duran AJ, del Pobil AP. Integrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation. Sensors. 2021; 21(4):1437. https://doi.org/10.3390/s21041437

Chicago/Turabian Style

Yoneyama, Ryota, Angel J. Duran, and Angel P. del Pobil. 2021. "Integrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation" Sensors 21, no. 4: 1437. https://doi.org/10.3390/s21041437

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