Department of Computer Science, Jaume I University, 12071 Castellon, Spain.
Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, Korea.
Sensors (Basel). 2021 Feb 19;21(4):1437. doi: 10.3390/s21041437.
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.
深度学习是计算机视觉和机器学习的主流范例，但在机器人视觉应用中，其性能通常不如预期。问题在于机器人感知本质上是主动的，并且在许多应用领域中，相关数据通常很匮乏。这就需要新的深度学习方法，以便在较低的数据消耗成本下提供良好的性能。我们在这里使用新方法和三种不同的深度架构解决了仓库自动化中的单目深度估计问题。我们的结果表明，与标准的数据驱动深度学习相比，将传感器模型和与机器人主动视觉相关的先验知识相结合，可以从比通常更少的训练样本中持续改进结果和学习性能。
