Simultaneous Drone Localisation and Wind Turbine Model Fitting During Autonomous Surface Inspection We present a method for simultaneous localisation and wind turbine model fitting for a drone performing an automated surface inspection. We use a skeletal parameterisation of the turbine that can be easily integrated into a nonlinear least squares optimiser, combined with a pose graph representation of the dronexe2x80x99s 3D trajectory, allowing us to optimise both sets of parameters simultaneously. Given images from an onboard camera, we use a CNN to infer projections of the skeletal model, enabling correspondence constraints to be established through a cost function. This is then coupled with GPSIMU measurements taken at key frames in the graph to allow successive optimisation as the drone navigates around the turbine. We present two variants of the cost function, one based on traditional 2D point correspondences and the other on direct image interpolation within the inferred projections. Results from experiments on simulated and realworld data show that simultaneous optimisation provides improvements to localisation over only optimising the pose and that combined use of both cost functions proves most effective.

MultiSensor 6DoF Localization For Aerial Robots In Complex GNSSDenied Environments The need for robots autonomously navigating in more and more complex environments has motivated intense Ru0026 D efforts in making robot pose estimation more accurate and reliable. This paper presents a multisensor multihypothesis method for robust 6DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multihypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multicore implementation. The proposed method has been extensively validated for closedloop aerial robot navigation in different urban and industrial scenarios and has shown advantages over wellknown singlesensor techniques.