There has been widespread in the research of Unmanned Aerial Vehicles (UAV) in construction projects due to its miniature stature, agility, and mobility. Depending on the required function, UAVs can be programmed to operate as fully autonomous or semi-autonomous. However, there are significant challenges to achieve indoor navigation. Indoor navigation requires the UAV obstacle avoidance capabilities and a system to replace the outdoor Global Navigation Satellite System (GNSS) that senses the UAV location relative to geographical coordinates. This research explores a framework that combines the use of BIM data and sensing technologies to address accuracy, precision, and adaptability for indoor navigation in extreme construction project environments. The approach has three components: (1) Simultaneous localization and mapping (SLAM), (2) 3D reconstruction (S]sparse point-cloud to dense point-cloud), and (3) navigation. The approach combines the use of a monocular camera and inertial measurement unit (IMU) to address accuracy, precision, and adaptability for indoor navigation in extreme construction project environments. It incorporates a collision-avoidance system using Visual SLAM with pre-integrated IMU. It includes a sliding windows algorithm to estimate state and map and feed the path planning information for the UAV trajectory based on the A* algorithm. The project incorporates a robust design that is adequate for localization, mapping, 3D reconstruction, and navigation projects with higher resolution and accuracy requirements. This project develops the framework and the development of UAV automatic flight controller technology for the community of researchers.