Tracking the object 6-DoF pose is crucial for various downstream robot tasks and real-world applications. In this paper, we investigate the real-world robot task of aerial vision guidance for aerial robotics manipulation, utilizing category-level 6-DoF pose tracking. Aerial conditions inevitably introduce special challenges, such as rapid viewpoint changes in pitch and roll. To support this task and challenge, we firstly introduce a robust category-level 6-DoF pose tracker (Robust6DoF). This tracker leverages shape and temporal prior knowledge to explore optimal inter-frame keypoint pairs, generated under a priori structural adaptive supervision in a coarse-to-fine manner. Notably, our Robust6DoF employs a Spatial-Temporal Augmentation module to deal with the problems of the inter-frame differences and intra-class shape variations through both temporal dynamic filtering and shape-similarity filtering. We further present a Pose-Aware Discrete Servo strategy (PAD-Servo), serving as a decoupling approach to implement the final aerial vision guidance task. It contains two servo action policies to better accommodate the structural properties of aerial robotics manipulation. Exhaustive experiments on four well-known public benchmarks demonstrate the superiority of our Robust6DoF. Real-world tests directly verify that our Robust6DoF along with PAD-Servo can be readily used in real-world aerial robotic applications.

翻译：追踪物体的6自由度姿态对于各类下游机器人任务和实际应用至关重要。本文研究了航空机器人操作中利用类别级6D姿态追踪进行航空视觉引导的真实世界机器人任务。航空环境不可避免地引入了特殊挑战，例如俯仰和滚转方向上的快速视角变化。为支持该任务并应对上述挑战，我们首先提出了一种鲁棒的类别级6D姿态追踪器（Robust6DoF）。该追踪器利用形状先验和时间先验知识，在由粗到精的先验结构自适应监督下探索最优帧间关键点对。值得注意的是，我们的Robust6DoF采用时空增强模块，通过时间动态过滤与形状相似性过滤联合处理帧间差异和类内形状变化问题。我们进一步提出姿态感知离散伺服策略（PAD-Servo），作为实现最终航空视觉引导任务的一种解耦方法。该策略包含两种伺服动作策略，以更好地适配航空机器人操作的结构特性。在四个知名公开基准上的充分实验证明了Robust6DoF的优越性。实际测试直接验证了我们的Robust6DoF结合PAD-Servo可便捷应用于真实航空机器人场景。