This thesis investigates the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in GPS-denied urban environments, focusing on a Visual Inertial Odometry (VIO) algorithm developed from scratch for vehicle pose estimation. In contexts where GPS signals are limited or not available, such as urban areas, VIO provides a promising solution by integrating visual information from a monocular camera with inertial data from an Inertial Measurement Unit (IMU). A monocular visual-inertial system acts as the minimal sensor suite for six degrees-of-freedom (DOF) metric state estimation, offering advantages in size, cost and simplicity of hardware setup. The main objective of this research is to design a monocular visual-inertial odometry algorithm using a loosely coupled approach that enables drones to navigate autonomously using only onboard sensors.