Petschnigg Christina, Spitzner Markus, Weitzendorf Lucas, Pilz Jürgen
BMW Group, Department of Factory Planning, Knorrstraße 147, 80788 Munich, Germany.
Department of Statistics, Alpen-Adria-University Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, Austria.
Entropy (Basel). 2021 Mar 3;23(3):301. doi: 10.3390/e23030301.
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.
室内环境的三维建模和过程模拟的生成在工厂和装配规划中起着重要作用。在棕地规划案例中，现有数据往往过时且不完整，尤其是对于大多数以二维方式规划的老工厂而言。因此，当前的环境模型无法直接基于现有数据生成，而且几乎不存在一种以高度自动化方式构建此类工厂模型的整体方法。生成生产工厂环境模型的主要步骤包括数据收集、数据预处理、对象识别以及位姿估计。在这项工作中，我们详细阐述了一种系统的建模方法，该方法从大规模室内环境的数字化开始，以生成静态环境或模拟模型结束。对象识别步骤通过一个能够进行点云分割的贝叶斯神经网络来实现。我们详细阐述了贝叶斯分割框架估计的不确定性信息对生成的环境模型准确性的影响。在一个大规模汽车生产工厂装配线收集的真实数据集上，对数据收集和点云分割步骤以及由此产生的模型准确性进行了评估。贝叶斯分割网络明显超越了频率学派基线的性能，并使我们能够显著提高模拟场景中模型放置的准确性。
