Despite the advances in robotics a large proportion of the of parts handling tasks in the automotive industry's internal logistics are not automated but still performed by humans. A key component to competitively automate these processes is a 6D pose estimation that can handle a large number of different parts, is adaptable to new parts with little manual effort, and is sufficiently accurate and robust with respect to industry requirements. In this context, the question arises as to the current status quo with respect to these measures. To address this we built a representative 6D pose estimation pipeline with state-of-the-art components from economically scalable real to synthetic data generation to pose estimators and evaluated it on automotive parts with regards to a realistic sequencing process. We found that using the data generation approaches, the performance of the trained 6D pose estimators are promising, but do not meet industry requirements. We reveal that the reason for this is the inability of the estimators to provide reliable uncertainties for their poses, rather than the ability of to provide sufficiently accurate poses. In this context we further analyzed how RGB- and RGB-D-based approaches compare against this background and show that they are differently vulnerable to the domain gap induced by synthetic data.

翻译：尽管机器人技术取得了进步，汽车行业内物流中大量的零部件处理任务仍未实现自动化，而是由人工完成。在竞争环境中实现这些流程自动化的关键组件是六维位姿估计技术，该技术需能处理大量不同零部件、能以最少人工操作适配新零部件，并且具备满足工业要求的精度和鲁棒性。在此背景下，我们需要评估该技术在相关指标上的现状。为此，我们构建了一个具有代表性的六维位姿估计流水线，采用从经济可扩展的真实到合成数据生成到位姿估计器的先进组件，并在针对真实排序流程的汽车零部件上进行了评估。研究发现，尽管采用数据生成方法的六维位姿估计器性能表现良好，但未能达到工业要求。我们揭示其根本原因在于估计器无法为其位姿提供可靠的置信度，而非无法提供足够精确的位姿。在此基础上，我们进一步分析了基于RGB和RGB-D的方法在此背景下的对比表现，并表明它们对合成数据引起的域差异具有不同程度的脆弱性。