Localisation in GPS-denied environments is challenging and many existing solutions have infrastructural and on-site calibration requirements. This paper tackles these challenges by proposing a localisation system that is infrastructure-free and does not require on-site calibration, using a single active PTZ camera to detect, track and localise a circular LED marker. We propose to use a CNN trained using only synthetic images to detect the LED marker as an ellipse and show that our approach is more robust than using traditional ellipse detection without requiring tuning of parameters for feature extraction. We also propose to leverage the predicted elliptical angle as a measure of uncertainty of the CNN's predictions and show how it can be used in a filter to improve marker range estimation and 3D localisation. We evaluate our system's performance through localisation of a UAV in real-world flight experiments and show that it can outperform alternative methods for localisation in GPS-denied environments. We also demonstrate our system's performance in indoor and outdoor environments.

翻译：在GPS拒止环境中的定位具有挑战性，现有许多解决方案存在基础设施依赖和现场校准需求。本文通过提出一种无需基础设施和现场校准的定位系统来解决这些挑战，该系统使用单个主动PTZ相机检测、跟踪和定位圆形LED标记点。我们提出使用仅通过合成图像训练的CNN将LED标记点检测为椭圆，并证明该方法比需要调整特征提取参数的传统椭圆检测方法更具鲁棒性。我们还提出利用预测的椭圆角度作为CNN预测不确定性的度量，并展示其如何在滤波器中用于改进标记点距离估计和三维定位。通过无人机真实飞行实验评估系统性能，证明其在GPS拒止环境中的定位性能优于替代方法。同时展示了系统在室内外环境中的表现。