Monocular Depth Estimation With Improved Long-Range Accuracy for UAV Environment Perception

Environment perception by computing the depth is a key task for unmanned aerial vehicle (UAV) type systems. Due to the limited load they can carry, most drones are equipped with a single camera. This prevents general-purpose depth perception methods based either on light detection and ranging (LiDAR) or stereo reconstruction to be effectively used on such platforms. Due to the success of convolutional neural networks (CNNs), monocular depth estimation (MDE) methods have become more and more trustworthy, so their usage on drones is convenient. However, very few such methods have been proposed in the literature, mainly due to the few existing constraints and high diversity that unstructured aerial environments pose. To bridge this gap, we propose a novel approach for MDE, capable to work on aerial images. The method initially proposes an original CNN, particularly adapted to such scenarios. This is done by finding an optimal feature extractor, introducing a new scene understanding module, a new loss and a novel softmax transformation layer that facilitate a better convergence. Furthermore, since both short- and long-range accuracy is required for a robust UAV perception, we introduce a learning-based correction method that redistributes the depth points across the entire depth interval. The proposed CNN gives accurate results, while the additional refinement further improves the accuracy with only a few additional computational resources (around 1–2 ms). We initially show the capabilities of our method on synthetic images captured in unstructured aerial scenarios. Then, we prove that our method can work in real-life situations, computing depth from a single image (at multiple pitch angles) captured by a drone flying in a series of field and forest-like environments. In all these situations, the depth is densely estimated with increased accuracy and reliability.