Fast Monocular Depth Estimation for Autonomous Underwater Vehicles
Estimating an accurate depth map of a scene is essential for navigation and collision avoidance of autonomous vehicles. Although several methods exist and perform well above water, for the underwater environment this task is challenging not only due to the optical effect of the water but also due to the lack of datasets for deep learning methods.
In this research, we consider the problem of underwater dense depth completion, when sparse measurements are given, and with the aid of additional input images, the rest should be interpolated. In principle, two cameras can be mounted on a robot to compute stereo disparities. Yet, this is highly impractical for small, agile systems, as the required baseline significantly increases drag and limits the platform’s capabilities. Thus, we focus on monocular depth estimation with a high level of accuracy to enable safe navigation for an autonomous underwater vehicle (AUV) in real-time.
We suggest a training framework that gets input images from a monocular camera, jointly with sparse measurements generated from real-time SLAM. Our model uses an adjusted loss function that ensures a minimal error in the short navigation range. Alongside a depth map, our model outputs the uncertainty of the predictions, indicating the accuracy of each prediction.
We conduct experiments on a new dataset collected by the Marine Imaging Lab (Haifa University), and show that our architecture provides greater accuracy as the distance decreases, and hence can be applied for real-time navigation. By defining a new error measurement that estimates the percentage of inaccurate predictions that are deeper than the ground truth, we examine our model performance. We show that our model precision provides safe navigation for an AUV, with an error of 5% in a short range of up to 3[m]. Our proposed network runs on average at 3[fps] on an NVIDIA Jetson Xavier GPU.
* MSc seminar under the supervision of Prof. Guy Gilboa.