Perception with Guarantees: Certified Pose Estimation via Reachability Analysis

Abstract Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring the safety of these agents often requires localizing their 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., to guarantee safety even in extreme scenarios, and external services may additionally be untrustworthy. We address this problem by presenting an approach for 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.