LiDAR-odometry based UAV pose estimation in young forest environment

In this study, we propose a real-time pose estimation solution for Unmanned Aerial Vehicle in a seedling pine forest environment. Our method uses graph-based approach to fuse data from an onboard IMU sensors, a GNSS receiver and a 3D LiDAR. Features are detected from every LiDAR scan. A local map is built from the detected features and used to compute the LiDAR odometry in real time for the incoming scans. In order to obtain a robust estimate of the state of the vehicle, the noise covariance of the LiDAR odometry is updated at each iteration using the fitness score of the LiDAR. The proposed solution provides promising trajectory and velocity estimates even in GNSS denied scenario. Both the local and global consistencies of the estimated trajectory are encouraging.