Unsupervised Learning for Depth Estimation in Unstructured Environments

Environment perception through deep computation in unstructured environments is important for the construction of autonomous navigation systems. Most research focuses on navigation in structured scenes, including indoor mobility and driving along roads, while neglecting to consider unstructured environments, which often contain diverse heights and distributions. In addition, existing depth estimation algorithms based on deep learning often need to complete training under the supervision of Ground truth, and GT data with a large number of labels are not always easy to obtain. To tackle this issue, this paper proposes an unsupervised stereo depth estimation method for processing UAV navigation images in an unstructured environment. The method contains a primitive U-shaped CNN network architecture for processing such scenes. The feature extraction layer of the network is based on the YOLOv3 residual structure, and additional attention modules help the network enhance its ability to perceive image features. Finally, depth estimation experiments on the unstructured environments dataset Mid-Air further demonstrate the effectiveness and reliability of the proposed method.