The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots’ training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7° and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities.

中文翻译：

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使用热图像中的地平线检测进行姿态估计

缺乏冗余姿态传感器代表了许多低成本无人机的一个相当大但普遍的弱点。除了使用姿态传感器，利用地平线作为姿态控制的视觉参考也是人类飞行员训练的一部分。出于这个原因，并且考虑到图像传感器的理想特性，已经进行了大量研究，建议使用视觉传感器进行地平线检测，以便在无人机上获得冗余姿态估计。然而，大气和照明条件可能会阻碍可见光图像传感器的可操作性，甚至使其无法使用，例如在夜间。热红外图像传感器的工作条件范围更广，而且价格在过去几年大幅下降，在某些操作场景中成为可见光谱传感器的替代品。在本文中，提出了两种姿态估计方法。第一种方法包括一种新颖的方法来估计最适合热图像中地平线的线。然后使用无限地平线模型将所得线用于估计俯仰角和滚转角。第二种方法使用深度学习来使用来自热图像的原始像素强度来预测姿态角。为此，使用惯性导航系统的测量值训练了一种新颖的卷积神经网络架构。两种方法都被证明对于冗余姿态估计是有效的，提供低于 1.7° 的 RMS 误差并以高达 48 Hz 的频率运行，具体取决于所选的方法、输入图像分辨率和可用的计算能力。