  When manipulating an object to accomplish complex tasks, humans rely on both
vision and touch to keep track of the object's 6D pose. However, most existing
object pose tracking systems in robotics rely exclusively on visual signals,
which hinder a robot's ability to manipulate objects effectively. To address
this limitation, we introduce TEG-Track, a tactile-enhanced 6D pose tracking
system that can track previously unseen objects held in hand. From consecutive
tactile signals, TEG-Track optimizes object velocities from marker flows when
slippage does not occur, or regresses velocities using a slippage estimation
network when slippage is detected. The estimated object velocities are
integrated into a geometric-kinematic optimization scheme to enhance existing
visual pose trackers. To evaluate our method and to facilitate future research,
we construct a real-world dataset for visual-tactile in-hand object pose
tracking. Experimental results demonstrate that TEG-Track consistently enhances
state-of-the-art generalizable 6D pose trackers in synthetic and real-world
scenarios. Our code and dataset are available at
https://github.com/leolyliu/TEG-Track.
