Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle.

To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential frames from monocular videos are used to train the model. The model is composed of two subnetworks-the depth estimation subnetwork and the pose estimation subnetwork.