About
As part of my internship project, the objective was to design and implement a robust localization system capable of operating in outdoor GNSS-denied environments. The challenge was to achieve accurate pose estimation using onboard sensors while ensuring real-time performance.
The goal was to develop a 6DoF SLAM and sensor fusion solution that could provide consistent localization in the absence of GNSS, and integrate seamlessly with the robot’s main localization stack through an Extended Kalman Filter (EKF). Additionally, the system needed to support autonomous path planning based on the generated maps.
I developed and deployed a LiDAR–IMU-based SLAM system with real-time sensor fusion for 6DoF localization. The solution was field-tested on a real robot and outdoor environment, demonstrating reliable mapping and trajectory estimation under GNSS-denied conditions. I also implemented a path planning module using the SLAM-generated maps, enabling autonomous navigation in outdoor areas.