GPS-LiDAR Sensor Fusion Aided by 3D City Models for UAVs

Outdoor positioning for Unmanned Aerial Vehicles (UAVs) commonly relies on GPS signals, which might be reflected or blocked in urban areas. In such cases, additional on-board sensors such as Light Detection and Ranging (LiDAR) are desirable. To fuse GPS and LiDAR measurements, it is important, yet challenging, to accurately characterize the error covariance of the sensor measurements. In this paper, we propose a GPS-LiDAR fusion technique with a novel method for modeling the LiDARbased position error covariance as a function of the point cloud features. We use the LiDAR point clouds to estimate incremental motion, and to estimate global pose by matching with a 3D city model. For GPS measurements, we use the 3D city model to eliminate non-line-of-sight (NLOS) satellites and model the measurement covariance based on the received signal-to-noiseratio (SNR) values. Finally, we implement an Unscented Kalman Filter (UKF) to estimate the UAV’s global pose and analyze its observability. To validate our algorithm, we conduct outdoor experiments and demonstrate a clear improvement in the UAV’s global pose estimation. Keywords—Unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), 3-dimensional (3D) city model, global positioning system (GPS), unscented kalman filter (UKF)