  The ability of robots to autonomously navigate through 3D environments
depends on their comprehension of spatial concepts, ranging from low-level
geometry to high-level semantics, such as objects, places, and buildings. To
enable such comprehension, 3D scene graphs have emerged as a robust tool for
representing the environment as a layered graph of concepts and their
relationships. However, building these representations using monocular vision
systems in real-time remains a difficult task that has not been explored in
depth. This paper puts forth a real-time spatial perception system Mono-Hydra,
combining a monocular camera and an IMU sensor setup, focusing on indoor
scenarios. However, the proposed approach is adaptable to outdoor applications,
offering flexibility in its potential uses. The system employs a suite of deep
learning algorithms to derive depth and semantics. It uses a robocentric
visual-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 (NVIDIA
3080). This enhances decision-making efficiency and effectiveness in simple
camera setups, augmenting robotic system agility. We make Mono-Hydra publicly
available at: https://github.com/UAV-Centre-ITC/Mono_Hydra
