Ultra Low Latency Object Tracking for Tactile Internet

Abstract

Bilateral teleoperation with force feedback
aims to transmit human expertise over long distances by transferring the
sensation of physical contact. One of the primary challenges in achieving this
goal is the ultra low latency requirement. Tactile internet and model-mediated
teleoperation are promising research areas addressing the latency constraint. In
model-mediated teleoperation, it is crucial that the remote environment
parameters are tracked with minimal latency to update the local model. This
work investigates the potential of designing a cost-effective object tracking
solution that performs comparably to state-of-the-art systems while maintaining
low tracking latency. A method is proposed to streamline the design process by
leveraging assumptions about the tracking conditions. An object-tracking
algorithm that can effectively fuse high-frequency noisy inertial data (>1 kHz)
with delayed, low-frequency (30 Hz) but accurate camera data has been designed.
A practical system was constructed using cost-efficient components, and a
dataset was collected for testing and characterization. The system demonstrated
the ability to produce object pose estimates at 1 ms intervals. In experiments
along a single axis, the system achieved a mean positional estimation error of
0.1 cm and a mean orientation estimation error of 0.5◦. The algorithm also
successfully corrected for camera latencies of up to 350 ms while maintaining
accurate estimates. These results demonstrate the possibility of achieving a
low latency, accurate object tracking system while keeping component and computation
costs reasonable.