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The ability of robots to autonomously navigate through 3D environmentsdepends on their comprehension of spatial concepts, ranging from low-levelgeometry to high-level semantics, such as objects, places, and buildings. Toenable such comprehension, 3D scene graphs have emerged as a robust tool forrepresenting the environment as a layered graph of concepts and theirrelationships. However, building these representations using monocular visionsystems in real-time remains a difficult task that has not been explored indepth. This paper puts forth a real-time spatial perception system Mono-Hydra,combining a monocular camera and an IMU sensor setup, focusing on indoorscenarios. However, the proposed approach is adaptable to outdoor applications,offering flexibility in its potential uses. The system employs a suite of deeplearning algorithms to derive depth and semantics. It uses a robocentricvisual-inertial odometry (VIO) algorithm based on square-root information,thereby ensuring consistent visual odometry with an IMU and a monocular camera.This system achieves sub-20 cm error in real-time processing at 15 fps,enabling real-time 3D scene graph construction using a laptop GPU (NVIDIA3080). This enhances decision-making efficiency and effectiveness in simplecamera setups, augmenting robotic system agility. We make Mono-Hydra publiclyavailable at: https://github.com/UAV-Centre-ITC/Mono_Hydra

Mono-hydra: Real-time 3D scene graph construction from monocular camera input with IMU