Projekt- und Abschlussarbeiten

Current state-of-the-art methods for evaluating 6 degrees of freedom (6D) pose estimators have several significant limitations. Existing error metrics often yield near-zero errors even for inaccurate pose estimations and are highly dependent on the object point cloud used, leading to inconsistent results across different objects. Moreover, these metrics fail to account for false detections. Accurate evaluation of pose estimators is crucial for applications in robotics, augmented reality, and object manipulation, where reliable performance is essential. Evaluation is especially critical when analysing 6D pose estimators under disturbance, to gain insight on how the disturbances affect the pose estimator. This thesis introduces a novel error metric and evaluation score that can assess poses independently of the specific object and incorporate false detections. The proposed score is adjustable for various evaluation scenarios. A theoretical discussion, along with a use case analysing a 6D pose estimator under disturbances, demonstrates the advantages of the new evaluation method  compared to existing state-of-the-art approaches.