The ability to accurately estimate depth information is crucial for many autonomous applications to recognize the surrounded environment and predict the depth of important objects. One of the most recently used techniques is monocular depth estimation where the depth map is inferred from a single image. This paper improves the self-supervised deep learning techniques to perform accurate generalized monocular depth estimation. The main idea is to train the deep model to take into account a sequence of the different frames, each frame is geotagged with its location information. This makes the model able to enhance depth estimation given area semantics. We demonstrate the effectiveness of our model to improve depth estimation results. The model is trained in a realistic environment and the results show improvements in the depth map after adding the location data to the model training phase.

翻译：准确估计深度信息的能力对于许多自主应用程序识别环绕环境并预测重要天体的深度至关重要。最近使用的技术之一是单眼深度估计,其中从一个图像中推断出深度地图。本文改进了自我监督的深层学习技术,以进行准确的通用单眼深度估计。主要的想法是培训深层模型,以考虑到不同框架的顺序,每个框架用其位置信息进行地理标记。这使得模型能够根据区域语义加强深度估计。我们展示了模型在提高深度估计结果方面的有效性。模型在现实环境中接受培训,结果显示在将定位数据添加到模型培训阶段之后深度地图的改进。