Inspired by the ability of humans to perceive the surface texture of unfamiliar objects without relying on vision, the sense of tactile can play a crucial role in the process of robots exploring the environment, especially in some scenes where vision is difficult to apply or occlusion is inevitable to exist. Existing tactile surface reconstruction methods rely on external sensors or have strong prior assumptions, which will limit their application scenarios and make the operation more complex. This paper presents a surface reconstruction algorithm that uses only a new vision-based tactile sensor where the surface structure of an unfamiliar object is reconstructed by multiple tactile measurements. Compared with existing algorithms, the proposed algorithm doesn't rely on external devices and focuses on improving the reconstruction accuracy of the large-scale object surface. Aiming at the difficulty that the reconstruction accuracy is easily affected by the pressure of sampling, we propose a correction algorithm to adapt it. Multi-frame tactile imprints generated from many times contact can accurately reconstruct global object surface by jointly using the point cloud registration algorithm, loop-closure detection algorithm based on deep learning, and pose graph optimization algorithm. Experiments verify the proposed algorithm can achieve millimeter-level accuracy in reconstructing the surface of interactive objects and provide accurate tactile information for the robot to perceive the surrounding environment.
翻译:受人类感知不为人知的物体表面质地的能力的启发, 不依赖视觉, 触觉感在机器人探索环境的过程中可以发挥关键作用, 特别是在难以应用视觉或不可避免存在隐蔽的场景中。 现有的触觉表面重建方法依靠外部传感器, 或具有很强的先前假设, 这会限制其应用情景, 并使操作更加复杂。 本文展示了一种地表重建算法, 只有使用基于视觉的新触觉传感器, 才能通过多重触觉测量重建不为人知的物体表面结构。 与现有的算法相比, 提议的算法并不依赖外部设备, 并侧重于提高大型物体表面的重建准确性。 着眼于重建准确性很容易受到取样压力的影响这一困难, 我们提出一个校正算法来调整它。 从许多次接触中生成的多框架触觉印本可以通过点云登记算法、 以深度学习为基础的循环测算法来精确地测量全球物体表面。 实验能在模拟的地面天平面天平面天算法中, 提供重新定位的精确度, 和精确性测算法, 提供模拟测算法。