A Depth Estimation Method for Ground Moving Platforms via Detecting Region of Interest

Depth estimation is an essential part of decentralized coordinated control of multiple moving platforms, and many studies on depth reconstruction use machine learning methods to obtain depth information directly. However, the obtained target depth value has high uncertainty, which will lead to errors. This paper proposes a depth estimation algorithm for ground moving platforms, which can quickly estimate the relative position of its neighbor. The depth estimation algorithm consists of two parts. The detection part uses a deep convolutional neural network to extract the region of interest (ROI) while the depth recovery part estimates the depth value of the points obtaining from the feature extractor, which only processes the features in ROI. Then we feed 3D points into a depth optimizer to remove the outliers. Finally, the experiment results are presented to verify the effectiveness of our depth estimation algorithm.