Adaptive covariance estimation of LiDAR-based positioning errors for UAVs

Outdoor positioning for unmanned aerial vehicles (UAVs) commonly relies on global navigation satellite system (GNSS) signals, which might be reflected or blocked in urban areas. Thus, additional on-board sensors such as light detection and ranging (LiDAR) are desirable to aid positioning. To fuse measurements from different sensors, it is important to accurately characterize the error covariance matrices of individual sensor measurements. We propose a novel method for adaptively estimating the LiDAR-based positioning error covariance matrix based on the point cloud features surrounding the UAV. We model the position error as a multivariate Gaussian distribution and estimate its covariance matrix from individual surface and edge feature points. Simulations show that our model is more accurate than a distance-based covariance matrix model. Furthermore, we conduct an outdoor experiment that fuses global positioning system (GPS) signals and LiDAR position measurements. We demonstrate a clear improvement in the UAV's global position estimation using our adaptive covariance matrix for LiDAR-based measurements.