标题： 通过先验知识集成的点云数据6D位姿估计：自主拆卸中的案例研究 显示英文标题

标题： 6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly

摘要： 在计算机视觉领域，即使利用三维点云数据，六维位姿的精确估计仍然是一个具有挑战性的任务。相比之下，在制造领域，有时利用先验知识可以推动这一领域的进展。本研究专注于起动机的拆解，以延长产品的生命周期工程。在此背景下，一个关键目标是识别固定在电机上的螺栓并估计其六维位姿，从而在制造工作流中实现自动化拆解。然而，由于遮挡的存在和单视图数据采集的局限性（特别是当电机被放置在夹具系统中时），某些部分会被遮挡，一些螺栓变得不可见。因此，开发一个能够获取完整螺栓信息的综合管道至关重要，以避免在螺栓检测中遗漏。在本文中，我们以项目范围内螺栓检测任务作为相关用例，介绍了一个精心设计的管道。这个多阶段管道有效地捕捉了电机上所有螺栓的六维信息，展示了如何有效利用先验知识来处理这一具有挑战性的任务。所提出的方法不仅有助于六维位姿估计领域的发展，还强调了整合特定领域见解以解决制造和自动化中复杂问题的可行性。 显示英文摘要

摘要： The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of product life cycles. A pivotal objective in this context involves the identification and 6D pose estimation of bolts affixed to the motors, facilitating automated disassembly within the manufacturing workflow. Complicating matters, the presence of occlusions and the limitations of single-view data acquisition, notably when motors are placed in a clamping system, obscure certain portions and render some bolts imperceptible. Consequently, the development of a comprehensive pipeline capable of acquiring complete bolt information is imperative to avoid oversight in bolt detection. In this paper, employing the task of bolt detection within the scope of our project as a pertinent use case, we introduce a meticulously devised pipeline. This multi-stage pipeline effectively captures the 6D information with regard to all bolts on the motor, thereby showcasing the effective utilization of prior knowledge in handling this challenging task. The proposed methodology not only contributes to the field of 6D pose estimation but also underscores the viability of integrating domain-specific insights to tackle complex problems in manufacturing and automation.