  In this paper, a deep learning approach is proposed to accurately position
wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived
environments. In the absence of GNSS signals, information on the speed of the
wheels of a vehicle (or other robots alike), recorded from the wheel encoder,
can be used to provide continuous positioning information for the vehicle,
through the integration of the vehicle's linear velocity to displacement.
However, the displacement estimation from the wheel speed measurements are
characterised by uncertainties, which could be manifested as wheel slips or/and
changes to the tyre size or pressure, from wet and muddy road drives or tyres
wearing out. As such, we exploit recent advances in deep learning to propose
the Wheel Odometry neural Network (WhONet) to learn the uncertainties in the
wheel speed measurements needed for correction and accurate positioning. The
performance of the proposed WhONet is first evaluated on several challenging
driving scenarios, such as on roundabouts, sharp cornering, hard-brake and wet
roads (drifts). WhONet's performance is then further and extensively evaluated
on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration,
respectively over a total distance of 493 km. The experimental results obtained
show that the proposed method is able to accurately position the vehicle with
up to 93% reduction in the positioning error of its original counterpart after
any 180s of travel. WhONet's implementation can be found at
https://github.com/onyekpeu/WhONet.
