Airborne smoothing and mapping using vision and inertial sensors
M. Bryson · M. Johnson-Roberson · S. Sukkarieh
TL;DR
A drone fuses IMU, GPS, and camera data with least-squares smoothing to estimate its path and build textured 3D terrain maps.
BRIEF
Low-flying drones can survey large areas of land, but turning their sensor data into an accurate 3D terrain model is difficult. This work combines readings from an inertial sensor, GPS, and a single camera on the aircraft. A smoothing algorithm based on nonlinear least squares estimates the vehicle's path and a dense set of terrain points together, rather than one at a time. A textured mesh surface is then built from those points to visualise the terrain. Such reconstructions can support environmental monitoring, including invasive plant detection and biomass mapping.
Abstract
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.