Abstract We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contact-based detection method tends to induce a degree of uncertainty with false-positive contact data because the sensors may react not only to actual contact but also to the unstable behavior of the robot. Therefore, it is crucial to consider a robust shape estimation method capable of handling such false-positive contact data. To realize this, we introduce the concept of heteroscedasticity into the contact data and propose a robust shape estimation algorithm based on Gaussian process implicit surfaces (GPIS). We confirmed that our algorithm not only reduces shape estimation errors caused by false-positive contact data but also distinguishes false-positive contact data more clearly than the GPIS through simulations and actual experiments using a quadcopter.

中文翻译：

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具有假阳性接触检测的稳健形状估计

摘要 我们提出了一种使用加速度计代替触觉传感器进行全方位接触检测的方法，用于使用触摸进行物体形状估计。与触觉传感器不同，我们基于接触的检测方法倾向于通过假阳性接触数据引起一定程度的不确定性，因为传感器不仅可能对实际接触做出反应，而且可能对机器人的不稳定行为做出反应。因此，考虑一种能够处理此类假阳性接触数据的稳健形状估计方法至关重要。为了实现这一点，我们将异方差的概念引入到接触数据中，并提出了一种基于高斯过程隐式曲面（GPIS）的鲁棒形状估计算法。