Using aerial images to calibrate the inertial sensors of a low-cost multispectral autonomous remote sensing platform (AggieAir)

Even though small, low-cost unmanned aerial vehicles (UAVs) make good remote sensing platforms by reducing the cost and making imagery easier to obtain, there are also some tradeoffs. The low altitude, small image footprint and high number of images make it difficult and tedious to georeference the images based on features. Auto-orthorectification techniques based on the position and attitude of the UAV would work well except the inherent errors in the UAV sensors reduce the accuracy of the orthorectification significantly. This paper presents a method to improve the orthorectification accuracy by calibrating the UAV sensors. This is done by inverse orthorectifing the images to find the actual position and attitude of the UAV using ground references setup in a square. Actual data from a test flight is used to validate this method.