Abstract
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 non-linear least squares optimiser, combined with a pose graph representation of the drone's 3-D 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 GPS/IMU 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 real-world 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.
Abstract (translated)
我们提出了一种同时定位和风力涡轮模型适合无人机进行自动表面检查的方法。我们使用涡轮的骨架参数化，可以很容易地集成到非线性最小二乘优化器中，结合无人机三维轨迹的姿态图表示，允许我们同时优化这两组参数。根据机载摄像机的图像，我们使用CNN推断骨骼模型的投影，从而通过成本函数建立对应约束。然后再结合在图中关键帧处进行的GPS/IMU测量，以便在无人机在涡轮周围导航时进行连续优化。我们提出了两种不同的成本函数，一种是基于传统的二维点对应关系，另一种是基于推断投影内的直接图像插值。对模拟和现实数据的实验结果表明，同时优化比仅优化姿势更能改善局部化，并且两种成本函数的结合使用证明是最有效的。
URL
https://arxiv.org/abs/1904.04523
