Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation.
Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation.
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DOI：
10.1109/lra.2017.2654542
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发表时间：
2017-04
影响因子：
5.2
通讯作者：
Murphey TD
中科院分区：
文献类型：
--
作者：
Abraham I;Prabhakar A;Hartmann MJZ;Murphey TD
目前估计物体形状的方法（使用视觉或触摸）通常依赖于高分辨率传感。在这里，我们利用遍历探索证明成功的形状估计时，使用低分辨率的二进制接触传感器。该测量模型是作为一个基于碰撞的触觉测量，分类方法被用来区分形状的边界区域在搜索空间。测量模型的后验似然估计帮助系统主动寻找二元传感器最有可能返回信息测量的区域。结果表明，成功的形状估计的各种对象，以及在一个环境中识别多个对象的能力。有趣的是，遍历探索利用非接触运动来收集有关形状的重要信息。该算法是在三维模拟扩展，我们提出了二维实验结果使用Rethink巴克斯特机器人。
Current methods to estimate object shape—using either vision or touch—generally depend on high-resolution sensing. Here, we exploit ergodic exploration to demonstrate successful shape estimation when using a low-resolution binary contact sensor. The measurement model is posed as a collision-based tactile measurement, and classification methods are used to discriminate between shape boundary regions in the search space. Posterior likelihood estimates of the measurement model help the system actively seek out regions where the binary sensor is most likely to return informative measurements. Results show successful shape estimation of various objects as well as the ability to identify multiple objects in an environment. Interestingly, it is shown that ergodic exploration utilizes non-contact motion to gather significant information about shape. The algorithm is extended in three dimensions in simulation and we present two dimensional experimental results using the Rethink Baxter robot.