Airborne smoothing and mapping using vision and inertial sensors

This paper presents a framework for 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 terrain reconstructions. Our method seeks to integrate all of the sensor information using a statistically optimal non-linear least squares smoothing algorithm to estimate vehicle poses simultaneously to a dense point feature map of the terrain. A visualisation of the terrain structure is then created by building a textured mesh-surface from the estimated point features. The resulting terrain reconstruction can be used for a range of environmental monitoring missions such as invasive plant detection and biomass mapping.