  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.
