Scene Dynamics Prediction using Physically based Simulation (P5)

使用基于物理的模拟进行场景动态预测 (P5)

• 批准号: 333400996
• 负责人: Professor Dr. Reinhard Klein
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/333400996?language=en
• 关键词: Scene; Dynamics; Prediction; using; Physically

The first phase of the project focused on the real-time reconstruction of static scenes and the creation of activity maps. For this purpose, geometric data as well as data on material properties were used. Both data sources provide valuable information for applications such as service robot navigation and object manipulation. The main goal of the second project phase is the anticipation of scene dynamics. The planned investigations cover 3D reconstruction, physical-based simulation including inference of material parameters in dynamic environments in real time. In order to focus our research, we first limit ourselves to anticipating the dynamics of textiles as required by robots to properly handle textiles. To this end, we will first extend the approaches to real-time reconstruction of static scenes developed in the first project phase to include scene dynamics and the deformation of objects. In addition to the reconstruction of the rough geometry, we also plan to capture the underlying structures of the textiles, such as weaving or knitting patterns of fabrics, in order to derive physical material parameters that are relevant for the dynamic behaviour. The focus is on a differentiable physically based fabric simulation. The resulting simulation is to be combined with new approaches for efficient 3D scene segmentation in order to derive a parametrized model that fits to the observed data and allow for the prediction of the corresponding dynamics of objects. The latter enables e.g. a robot to compare the anticipated movement of a textile when gripping it with real observations and to correct it if necessary.

该项目的第一阶段侧重于静态场景的实时重建和活动地图的创建。为此，使用了几何数据以及材料特性数据。这两个数据源都为服务机器人导航和对象操作等应用提供了有价值的信息。第二个项目阶段的主要目标是场景动态的预期。计划中的调查包括三维重建、基于物理的模拟，包括真实的动态环境中材料参数的推断。为了集中我们的研究，我们首先将自己限制在机器人正确处理纺织品所需的纺织品动力学方面。为此，我们将首先扩展的方法，在第一个项目阶段开发的静态场景的实时重建，包括场景动态和物体的变形。除了重建粗糙的几何形状，我们还计划捕捉纺织品的底层结构，如编织或针织图案的织物，以获得物理材料参数，是相关的动态行为。重点是基于物理的可微分织物模拟。由此产生的模拟将与有效的3D场景分割的新方法相结合，以导出适合观察到的数据的参数化模型，并允许预测对象的相应动态。后者使得例如机器人能够将抓取纺织品时纺织品的预期运动与真实的观察结果进行比较，并且在必要时对其进行校正。