Abstract
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.
Abstract (translated)
在物理与数字世界融合的系统中，代理（agents）越来越多地承担安全关键性的任务。确保这些代理的安全性通常需要精确定位其姿态以便后续操作。姿态估计可以基于激光雷达传感器、摄像头以及如GPS等外部服务的不同组合来获取。然而，在涉及人身安全的关键领域中，粗略的姿态估计是不够的；在最坏的情况下也无法保证安全性，并且外部服务可能不可信。 为了应对这一挑战，我们提出了一种仅通过相机图像和已知目标几何结构来进行认证的姿态估计方法。这种方法通过对姿态进行形式化界定实现：利用近期可达到性分析（reachability analysis）及形式神经网络验证的成果来计算姿态。我们的实验表明，在合成环境与真实世界环境中，该方法能够有效地且准确地定位代理。 通过这种方式，即便是在缺乏可靠外部服务的情况下，也能确保安全关键任务中的精确性和可靠性。这种方法为实现更加自主、安全和有效的自动化系统开辟了新的途径。
URL
https://arxiv.org/abs/2602.10032
