Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing the pose for subsequent actions. Pose estimates can, e.g., be obtained from various combinations of lidar sensors, cameras, and external services such as GPS. Crucially, in safety-critical domains, a rough estimate is insufficient to formally determine safety, i.e., guaranteeing safety even in the worst-case scenario, and external services might additionally not be trustworthy. We address this problem by presenting a certified pose estimation in 3D solely from a camera image and a well-known target geometry. This is realized by formally bounding the pose, which is computed by leveraging recent results from reachability analysis and formal neural network verification. Our experiments demonstrate that our approach efficiently and accurately localizes agents in both synthetic and real-world experiments.

翻译：信息物理系统中的智能体日益承担安全关键任务。确保这些智能体的安全性通常需要为后续行动确定位姿。位姿估计可通过多种方式获得，例如结合激光雷达传感器、摄像头以及GPS等外部服务。关键在于，在安全关键领域，粗略估计不足以形式化地判定安全性（即在最坏情况下仍能保证安全），且外部服务可能并不可靠。我们通过提出一种仅利用相机图像和已知目标几何结构的三维认证位姿估计方法来解决该问题。该方法通过形式化界定位姿范围实现，其计算过程融合了可达性分析和形式化神经网络验证的最新研究成果。实验表明，我们的方法在合成场景与真实场景中均能高效精确地实现智能体位姿定位。