Understanding the 3D space is crucial for autonomous vehicles for planning and navigation. Traditionally autonomous vehicles use LiDAR sensor to 3D map its environment. LiDAR sensor data are often noisy and sparse making it not fully reliable for real-time applications like autonomous driving, thus redundant such sensors are used for the purpose. The array of cameras in an autonomous vehicle purposed for detection and tracking can be reused for depth estimation as well. In this paper, an unsupervised monocular depth estimation approach for autonomous vehicles which can be used as redundant depth estimators replacing multiple LiDAR sensors. Here, a deep learning based method is used with a multiscale encoder-decoder network to estimate depth. Target view among the stereo pairs is reconstructed by inverse warping the source view using geometric camera projection. The network is guided by the stereo positive–negative(SPN) loss which minimizes the loss between reconstructed view and corresponding stereo ground truth and, also maximizes the loss between reconstructed views and corresponding opposite stereo ground truth. The proposed approach shows state of the art accuracy in autonomous driving dataset KITTI.

了解3D空间对于自动驾驶汽车进行计划和导航至关重要。传统上，自动驾驶汽车使用LiDAR传感器对环境进行3D映射。LiDAR传感器数据通常是嘈杂且稀疏的，因此对于诸如自动驾驶之类的实时应用而言并不完全可靠，因此为此目的使用了冗余的此类传感器。自动驾驶汽车中用于检测和跟踪的摄像机阵列也可以重新用于深度估计。本文提出了一种用于自动驾驶汽车的无监督单眼深度估计方法，该方法可以用作替代多个LiDAR传感器的冗余深度估计器。在这里，基于深度学习的方法与多尺度编码器/解码器网络一起使用以估计深度。立体对之间的目标视图是通过使用几何摄影机投影对源视图进行反向扭曲来重建的。该网络受立体正负（SPN）损耗的引导，该损耗使重构视图和相应的立体地面真相之间的损耗最小，并且还使重构视图和相对的相对立体地面真相之间的损耗最大化。所提出的方法显示了自动驾驶数据集KITTI中的最新准确性。