In-situ unmanned aerial vehicle (UAV) sensor calibration to improve automatic image orthorectification

Small, low-altitude unmanned aerial vehicles (UAV)s can be very useful in many ecological applications as a personal remote sensing platform. However, in many cases it is difficult to produce a single georeferenced mosaic from the many small images taken from the UAV. This is due to the lack of features in the images and the inherent errors from the inexpensive navigation sensors. This paper focuses on improving the orthorectification accuracy by finding these errors and calibrating the navigation sensors. This is done by inverse-orthorectifying a set of images collected during flight using ground targets and General Procrustes Analysis. By comparing the calculated data from the inverse-orthorectification and the measured data from the navigation sensors, different sources of errors can be found and characterized, such as GPS computational delay, logging delay, and biases. With this method, the orthorectification errors are reduced from less than 60m to less than 1.5m.