Depth Completion in Unseen Field Robotics Environments Using Extremely Sparse Depth Measurements

Autonomous field robots operating in unstructured environments require robust perception to ensure safe and reliable operations. Recent advances in monocular depth estimation have demonstrated the potential of low-cost cameras as depth sensors; however, their adoption in field robotics remains limited due to the absence of reliable scale cues, ambiguous or low-texture conditions, and the scarcity of large-scale datasets. To address these challenges, we propose a depth completion model that trains on synthetic data and uses extremely sparse measurements from depth sensors to predict dense metric depth in unseen field robotics environments. A synthetic dataset generation pipeline tailored to field robotics enables the creation of multiple realistic datasets for training purposes. This dataset generation approach utilizes textured 3D meshes from Structure from Motion and photorealistic rendering with novel viewpoint synthesis to simulate diverse field robotics scenarios. Our approach achieves an end-to-end latency of 53 ms per frame on a Nvidia Jetson AGX Orin, enabling real-time deployment on embedded platforms. Extensive evaluation demonstrates competitive performance across diverse real-world field robotics scenarios.

Radar Meets Vision: Robustifying Monocular Metric Depth Prediction for Mobile Robotics

Mobile robots require accurate and robust depth measurements to understand and interact with the environment. While existing sensing modalities address this problem to some extent, recent research on monocular depth estimation has leveraged the information richness, yet low cost and simplicity of monocular cameras. These works have shown significant generalization capabilities, mainly in automotive and indoor settings. However, robots often operate in environments with limited scale cues, self-similar appearances, and low texture. In this work, we encode measurements from a low-cost mmWave radar into the input space of a state-of-the-art monocular depth estimation model. Despite the radar's extreme point cloud sparsity, our method demonstrates generalization and robustness across industrial and outdoor experiments. Our approach reduces the absolute relative error of depth predictions by 9-64% across a range of unseen, real-world validation datasets. Importantly, we maintain consistency of all performance metrics across all experiments and scene depths where current vision-only approaches fail. We further address the present deficit of training data in mobile robotics environments by introducing a novel methodology for synthesizing rendered, realistic learning datasets based on photogrammetric data that simulate the radar sensor observations for training. Our code, datasets, and pre-trained networks are made available at https://github.com/ethz-asl/radarmeetsvision.