A comparison of feature and pose-based mapping using vision, inertial and GPS on a UAV

This paper presents and compares two different approaches to integrating sensor information from an Inertial Measuring Unit (IMU), Global Positioning System (GPS) receiver and monocular vision camera mounted to a low-flying Unmanned Aerial Vehicle (UAV) for building large-scale 3D terrain reconstructions. Both approaches utilise a statistically optimal bundle adjustment formulation that incorporates Vision, IMU and GPS observations into the map and pose optimisation process. Our first approach employs a novel pose-only formulation that optimises relative camera poses based on vision feature matches between frames, while incorporating IMU and GPS information. Our approach is related to, but differs from existing pose-graph techniques by formulating a set of 1D epipolar constraints for features matched between two camera frames, rather than minimising 4D feature re-projection errors, or marginalising feature states. We compare results of the method to a second approach which estimates both 3D features and poses together, using airborne vision, IMU and GPS data collected in an ecology mapping application. The results demonstrate a reduction in the computational complexity during optimisation for the pose-only approach, while producing equivalent accuracy in the reconstructed 3D terrain map.