Fast terrain mapping from low altitude digital imagery

Abstract

We present a linear time Real Terrain Reconstruction (RTR) framework for fixed-wing micro aerial vehicles (MAVs) in this paper. Single-shot aerial images labeled with GPS and IMU signals are acquired by a fixed-wing MAV in several flights. Then these images are fed into our structure from motion (SfM) processing to generate accuracy pose estimation and 3D points. RTR improves existing state of the art algorithms VisualSFM [1] in multiaspect so as to make it more suitable for large-scale terrain reconstruction from aerial imagery. Firstly, we present a novel strategy of combining signals from airborne sensors (GPS/IMU) with the traditional SfM method, which can improve speed and accuracy of pose estimation observably. Secondly, a delayed aerial triangular method is designed to reconstruct a point visible in more than two cameras with an appropriate baseline. Thirdly, we also release 5 aerial imagery datasets which contain over 15 thousands images totally with the detailed MAV pose information from airborne sensors (GPS/IMU). These resources can be used as a new benchmark to facilitate further research in the area. We test our algorithm on these aerial image sets with various settings, and show that RTR offers state of the art performance for large-scale terrain reconstructions.