论文标题
中间：蒙特卡洛对滑动触摸的分布推断
MidasTouch: Monte-Carlo inference over distributions across sliding touch
论文作者
论文摘要
我们提出了MidAstouch，这是一种触觉感知系统，用于在线全球定位基于视觉的触摸传感器在对象表面上滑动。该框架随着时间的推移带来了触觉的图像，并在不需要视觉先验的情况下输出传感器姿势不断发展的分布。我们的主要见解是通过触觉传感估算局部表面几何形状，为其学习紧凑的表示，并在很长一段时间内消除这些信号。 MidAstouch的骨干是一种蒙特卡洛粒子滤波器，其测量模型基于从触觉模拟中学到的触觉代码网络。该网络的灵感来自LIDAR位置识别，它紧凑地总结了局部表面几何形状。这些生成的代码与预先计算的触觉代码簿有效比较，以更新姿势分布。我们进一步发布了现实世界中的YCB-Slide数据集，并在基于视觉的触觉传感器和标准YCB对象之间模拟了有力的滑动相互作用。虽然单点触摸的定位本质上是模棱两可的，但我们可以通过穿越显着的表面几何形状来快速地定位我们的传感器。项目页面：https：//suddhu.github.io/midastouch-tactile/
We present MidasTouch, a tactile perception system for online global localization of a vision-based touch sensor sliding on an object surface. This framework takes in posed tactile images over time, and outputs an evolving distribution of sensor pose on the object's surface, without the need for visual priors. Our key insight is to estimate local surface geometry with tactile sensing, learn a compact representation for it, and disambiguate these signals over a long time horizon. The backbone of MidasTouch is a Monte-Carlo particle filter, with a measurement model based on a tactile code network learned from tactile simulation. This network, inspired by LIDAR place recognition, compactly summarizes local surface geometries. These generated codes are efficiently compared against a precomputed tactile codebook per-object, to update the pose distribution. We further release the YCB-Slide dataset of real-world and simulated forceful sliding interactions between a vision-based tactile sensor and standard YCB objects. While single-touch localization can be inherently ambiguous, we can quickly localize our sensor by traversing salient surface geometries. Project page: https://suddhu.github.io/midastouch-tactile/
