Particle Filters (PFs) have been successfully used in three‐dimensional (3D) model‐based pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two‐stage 3D model‐based approach based on a PF for single‐frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm‐based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask‐based) based on the UAV Computer‐Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed‐wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results. The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.

中文翻译：

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基于两阶段3D模型的无人机姿态估计：优化方法的比较

粒子滤波器（PF）已成功用于基于三维（3D）模型的姿态估计中。通常，这些过滤器取决于重要性加权的计算，该重要性加权使用相似性度量作为代理来近似似然函数。在本文中，我们探索基于PF的基于两阶段3D模型的方法用于单帧姿态估计。首先，我们使用在无人飞行器（UAV）检测的合成数据集中训练的分类器和通过边界框属性索引的预训练数据库来获得初始粗略姿态估计。其次，我们采用优化算法来优化所使用的相似性指标并减少获得的误差。我们测试了四种不同的算法：（a）粒子滤波器优化（PFO），（b）粒子群优化（PSO），（c）修改后的PSO，（d）一种基于遗传算法中存在的进化策略的方法，称为遗传算法框架（GAbF）。为了检查每次迭代的估计质量，我们基于UAV计算机辅助设计（CAD）模型测试了多个相似性指标（基于颜色，边缘和蒙版）。该框架适用于固定翼无人机在户外快速巡逻艇（FPB）中自动降落的户外姿态估计。我们通过采用更好的问题表述，使用深度神经网络（DNN）进行无人飞行器检测，对使用的相似性指标进行比较，比较姿态优化方案并显示新结果，扩展了以前的方法。未来的工作将集中于将此方案包含在跟踪体系结构中，以提高观察之间结果的准确性。