Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation
This work addresses a specific problem in robotics for bin-picking tasks, offering an incremental improvement by enhancing training data synthesis.
The paper tackles the challenge of 6D object pose estimation in bin-picking applications by proposing a model-agnostic hard example synthesis method that targets high-error regions in pose and occlusion space, resulting in up to a 20% improvement in correct detection rate on the ROBI-dataset.
6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and occlusion between objects of the same type may cause confusion even in well-trained models. We propose a novel method of hard example synthesis that is model-agnostic, using existing simulators and the modeling of pose error in both the camera-to-object viewsphere and occlusion space. Through evaluation of the model performance with respect to the distribution of object poses and occlusions, we discover regions of high error and generate realistic training samples to specifically target these regions. With our training approach, we demonstrate an improvement in correct detection rate of up to 20% across several ROBI-dataset objects using state-of-the-art pose estimation models.