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.

翻译：我们建议使用加速计而不是触摸感应器进行全天方向接触检测。 与触摸感应器不同,我们基于接触的检测方法往往会给假阳性接触数据带来一定程度的不确定性,因为传感器不仅对实际接触作出反应,而且对机器人的不稳定行为作出反应。 因此,考虑一种能够处理此类假阳性接触数据的稳健的形状估计方法至关重要。 为此,我们在接触数据中引入了超摄性概念,并提出了基于高斯进程隐含表面的强势形状估计算法。 我们确认,我们的算法不仅减少了假阳性接触数据造成的形状估计错误,而且还通过模拟和采用四分法进行的实际实验,比GPIS更清楚地区分了虚假阳性接触数据。