LiDAR-IMU SLAM: Autonomous UAV Localization in GPS-Denied Environments

This research investigates the implementation and simulation of a GPS-denied localization system for an unmanned aerial vehicle (UAV) using LiDAR, an Inertial Measurement Unit (IMU), and Simultaneous Localization and Mapping (SLAM). Multiple UAV flight trajectories are designed and simulated in randomly generated environments to produce realistic LiDAR and IMU measurements, which are processed by the SLAM algorithm to estimate the UAV's motion and reconstruct its trajectory. The results demonstrate the feasibility of accurate localization and environmental mapping without GPS, providing a foundation for autonomous UAV navigation in GPS-denied environments.

Intern: Nathaniel Loeffler

Mentor: Dr.Shunguang Wu (AOS/QPJ)