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.
翻译:虽然深层次的学习方法正在成为如何处理3D扫描和图像处理任务的研究标准,但处理这些数据的行业标准仍然是基于分析的。我们的论文声称,分析方法不够可靠,测试、更新和维护难度较小。本文侧重于6D对3D扫描中的垃圾箱进行估计的具体任务。因此,我们提出了一个高质量的数据集,其中包括合成数据和由结构化的光扫描仪收集的真实扫描,并附有精确的说明。此外,我们为6Dbin的估算提出了两种不同的方法,一种是工业标准的分析方法,一种是基线数据驱动方法。这两种方法都是交叉评价的,我们的实验表明,用合成数据进行实际扫描的培训将改进我们提议的以数据驱动的神经模型。本立场文件是初步的,因为对拟议的方法进行了培训,并对我们计划在今后推广的相对较小的初步数据集进行了评估。