论文标题
Gelstereo触觉感测的实时标记本地化学习
Real-Time Marker Localization Learning for GelStereo Tactile Sensing
论文作者
论文摘要
Visuotactile感应技术在触觉传感中变得越来越流行，但是现有标记检测定位方法的有效性仍有待进一步探讨。本文不是基于轮廓的BLOB检测，而是为Gelstereo Visuotactile Sensing提供了一个基于学习的标记定位网络，称为Marknet。具体而言，Marknet提出了一个网格回归体系结构，以结合Gelstereo标记的分布。此外，对标记合理性评估者（MRE）进行建模以筛选适当的预测结果。实验结果表明，与MRE结合使用的Marknet在接触区域的不规则标记中达到了93.90％的精度，这表现优于传统的基于轮廓的BLOB检测方法，其大幅度为42.32％。同时，提出的基于学习的标记定位方法可以通过GPU加速度提供的OPENCV库提供的BLOB检测界面获得更好的实时性能，我们认为这将导致各种机器人操纵任务中的可感知敏感性提高。
Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks.
