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

TitleIntegrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation
Publication TypeJournal Article
Year of Publication2021
AuthorsYoneyama, R, Duran, AJ, del Pobil, AP
JournalSensors
Volume21
Pagination1437
ISSN1424-8220
Keywords3D sensing, deep learning in sensing, monocular depth estimation, optic flow, robot sensors, vision/camera based sensors, warehouse automation
Abstract

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.

URLhttps://www.mdpi.com/1424-8220/21/4/1437
DOI10.3390/s21041437