论文标题
通过姿势不受限制的多行结构化视觉重建3D旋转结构的正常剖面剖面
Reconstructing normal section profiles of 3D revolving structures via pose-unconstrained multi-line structured-light vision
论文作者
论文摘要
火车的轮是3D旋转的几何结构。重建正常部分的轮廓是确定铁路安全社区中车轮的关键几何参数和磨损的有效方法。现有的重建方法通常需要一个以约束位置和姿势工作的传感器，灵活性差和有限的视图。本文提出了一个姿势不可约束的正常剖面曲线轮廓重建框架，该框架通过由多行结构的光视觉传感器获取的多个3D通用剖面，用于3D旋转结构。首先，我们建立了一个模型，以使用相应的点来估计3D旋转几何结构和正常截面轮廓的轴。然后，我们将模型嵌入到迭代算法中以优化相应的点，并最终重建准确的正常截面曲线。我们进行了实际实验，以重建3D车轮的正常截面轮廓。结果表明，我们的算法达到了0.068mm的平均精度，并且具有0.007mm的性能重复性。改变传感器的姿势变化也很健壮。我们提出的框架和模型被推广到任何3D Wheelpe的旋转组件。
The wheel of the train is a 3D revolving geometrical structure. Reconstructing the normal section profile is an effective approach to determine the critical geometric parameter and wear of the wheel in the community of railway safety. The existing reconstruction methods typically require a sensor working in a constrained position and pose, suffering poor flexibility and limited viewangle. This paper proposes a pose-unconstrained normal section profile reconstruction framework for 3D revolving structures via multiple 3D general section profiles acquired by a multi-line structured light vision sensor. First, we establish a model to estimate the axis of 3D revolving geometrical structure and the normal section profile using corresponding points. Then, we embed the model into an iterative algorithm to optimize the corresponding points and finally reconstruct the accurate normal section profile. We conducted real experiment on reconstructing the normal section profile of a 3D wheel. The results demonstrate that our algorithm reaches the mean precision of 0.068mm and good repeatability with the STD of 0.007mm. It is also robust to varying pose variations of the sensor. Our proposed framework and models are generalized to any 3D wheeltype revolving components.
