The ability for a robot to understand its surroundings is becoming more important in today’s
world. LiDARs are sensors that can create a dense point cloud of the surrounding environment. On
a mobile robot, a LiDAR itself is not sufficient to create an accurate map and localize itself inside of
that map. Fusing LiDAR data with IMU measurements using a Kalman filter allows for extremely
accurate mapping and odometry. FAST-LIO, a robust 3D LiDAR-Inertial Odometry and Mapping
algorithm which is implemented using the Robot Operating System 1 (ROS1), is unable to be
implemented on recent state-of-the-art robots which are mainly compatible with ROS2 (a more
stable and efficient version of ROS1). This is also made worse by the fact that ROS1 is reaching its
end of life and will no longer be supported in 2025. This necessitates the need for FAST-LIO to be
rewritten using the ROS2 framework. This thesis converts FAST-LIO from the ROS1 framework
to the ROS2 framework. The converted ROS2 package was tested using NVIDIA’s Isaac Sim,
a photo realistic and physically accurate simulation software for robotics, as well as using prerecorded LiDAR and IMU data from a multi-scenario data set for ground robots. The accuracy of
the mapping is visually tested using the results from both the data set and Isaac Sim. By comparing
the trajectory from the new FAST-LIO to the ground truth provided by the data set, I verified the
accuracy of the odometry and showed that the ROS2 version has a comparable accuracy to the
ROS1 version.
