Zero-Shot Metric Depth Estimation via Monocular Visual-Inertial Rescaling for Autonomous Aerial Navigation

-- This paper presents a methodology to predict metric depth from monocular RGB images and an inertial measurement unit (IMU). T o enable collision avoidance during autonomous flight, prior works either leverage heavy sensors (e.g., LiDARs or stereo cameras) or data-intensive and domain-specific fine-tuning of monocular metric depth estimation methods. In contrast, we propose several lightweight zero-shot rescaling strategies to obtain metric depth from relative depth estimates via the sparse 3D feature map created using a visual-inertial navigation system. These strategies are compared for their accuracy in diverse simulation environments. The best performing approach, which leverages monotonic spline fitting, is deployed in the real-world on a compute-constrained quadrotor . We obtain on-board metric depth estimates at 15 Hz and demonstrate successful collision avoidance after integrating the proposed method with a motion primitives-based planner . I. INTRODUCTION First Person View (FPV) drone pilots leverage a single forward-facing camera video stream transmitted over a radio feed and sensors embedded in the flight controller (e.g., IMU) to aggressively maneuver through dense clutter (e.g., through tree branches, under bridges, etc.).