Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans
This is an incremental contribution for robotic systems requiring robust bin pose estimation, but it is preliminary with a small dataset.
The paper tackles 6D pose estimation of bins in 3D scans by creating a dataset with synthetic and real data and proposing analytical and data-driven methods, showing that augmenting training with synthetic data improves the neural model.
An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.