PatchGraph: In-hand tactile tracking with learned surface normals
PatchGraph: In-hand tactile tracking with learned surface normals
复制标题
PatchGraph：使用学习的表面法线进行手持触觉跟踪
DOI：
10.1109/icra46639.2022.9811953
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Stuart Anderson
中科院分区：
文献类型：
--
作者：
Paloma Sodhi;M. Kaess;Mustafa Mukadam;Stuart Anderson
我们解决的问题，跟踪3D对象构成的触摸过程中的手操作。具体来说，我们着眼于使用基于视觉的触觉传感器，在接触点提供高维触觉图像测量跟踪小物体。虽然以前的工作依赖于先验信息的对象被本地化，我们删除这一要求。我们的关键见解是，一个对象是由几个局部表面补丁，每个信息足以实现可靠的对象跟踪。此外，我们可以通过提取嵌入在每个触觉图像中的局部表面法线信息来在线恢复该局部补丁的几何形状。我们提出了一个新的两阶段的方法。首先，我们使用图像平移网络学习从触觉图像到表面法线的映射。其次，我们使用这些表面法线的因素图重建一个局部补丁地图，并使用它来推断3D对象的姿态。我们展示了可靠的对象跟踪超过100个接触序列在独特的形状与四个对象在模拟和两个对象在现实世界中。
We address the problem of tracking 3D object poses from touch during in-hand manipulations. Specifically, we look at tracking small objects using vision-based tactile sensors that provide high-dimensional tactile image measurements at the point of contact. While prior work has relied on a-priori information about the object being localized, we remove this requirement. Our key insight is that an object is composed of several local surface patches, each informative enough to achieve reliable object tracking. Moreover, we can recover the geometry of this local patch online by extracting local surface normal information embedded in each tactile image. We propose a novel two-stage approach. First, we learn a mapping from tactile images to surface normals using an image translation network. Second, we use these surface normals within a factor graph to both reconstruct a local patch map and use it to infer 3D object poses. We demonstrate reliable object tracking for over 100 contact sequences across unique shapes with four objects in simulation and two objects in the real-world.
影响因子：
5.2
作者：
Czarnowski, Jan;Laidlow, Tristan;Davison, Andrew J.
通讯作者：
Davison, Andrew J.