State Estimation for Aerial Vehicles in Forest Environments

Abstract

Autonomous navigation of unnamed vehicles in a forest is a challenging task. In such environments, due to the canopies of the trees, GNSS-based navigation can be degraded or even unavailable. In this paper we propose a state estimation solution for aerial vehicles based on the fusion of GNSS, AHRS and LIDAR-based odometry. In our LIDAR odometry solution, the trunks of the trees are used in a feature-based scan-matching algorithm to estimate the relative movement of the vehicle. Our method uses a robust adaptive fusion algorithm based on the unscented Kalman filter. Experimental data collected during the navigation of a quadrotor in an actual forest environment is used to demonstrate the effectiveness of our approach.