LFP-Mono: Lightweight Self-Supervised Network Applying Monocular Depth Estimation to Low-Altitude Environment Scenarios

For UAVs, the industry currently relies on expensive sensors for obstacle avoidance. A significant challenge arises from the scarcity of high-quality depth estimation datasets tailored for low-altitude environments, which hinders the advancement of self-supervised learning methods in these settings. Furthermore, mainstream depth estimation models capable of achieving obstacle avoidance through image recognition are built upon convolutional neural networks or hybrid Transformers. Their high computational costs make deployment on resource-constrained edge devices challenging. While existing lightweight convolutional networks reduce parameter counts, they struggle to simultaneously capture essential features and fine details in complex scenes. In this work, we introduce LFP-Mono as a lightweight self-supervised monocular depth estimation network. In the paper, we will detail the Pooling Convolution Downsampling (PCD) module, Continuously Dilated and Weighted Convolution (CDWC) module, and Cross-level Feature Integration (CFI) module. All results show that LFP-Mono outperforms existing lightweight methods on the KITTI benchmark, and by evaluating with the Make3D dataset, show that our method generalizes outdoors. Finally, by training and testing on the Syndrone dataset, baseline work shows that LFP-Mono exceeds state-of-the-art methods for low-altitude drone performance.