No abstract

We propose an image-based cross-view geolocalization method that estimates the global pose of a UAV with the aid of georeferenced satellite imagery. Our method consists of two Siamese neural networks that extract relevant features despite large differences in viewpoints. The input to our method is an aerial UAV image and nearby satellite images, and the output is the weighted global pose estimate of the UAV camera. We also present a framework to integrate our crossview geolocalization output with visual odometry through a Kalman filter. We build a dataset of simulated UAV images and satellite imagery to train and test our networks. We show that our method performs better than previous camera pose estimation methods, and we demonstrate our networks ability to generalize well to test datasets with unseen images. Finally, we show that integrating our method with visual odometry significantly reduces trajectory estimation errors.

India in 2014. His research interests include safety, trajectory planning, and control for autonomous vehicles.

No abstract

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.