  This paper proposes a transfer learning approach to recalibrate our
previously developed Wheel Odometry Neural Network (WhONet) for vehicle
positioning in environments where Global Navigation Satellite Systems (GNSS)
are unavailable. The WhONet has been shown to possess the capability to learn
the uncertainties in the wheel speed measurements needed for correction and
accurate positioning of vehicles. These uncertainties may be manifested as tyre
pressure changes from driving on muddy and uneven terrains or wheel slips.
However, a common cause for concern for data-driven approaches, such as the
WhONet model, is usually the inability to generalise the models to a new
vehicle. In scenarios where machine learning models are trained in a specific
domain but deployed in another domain, the model's performance degrades. In
real-life scenarios, several factors are influential to this degradation, from
changes to the dynamics of the vehicle to new pattern distributions of the
sensor's noise, and bias will make the test sensor data vary from training
data. Therefore, the challenge is to explore techniques that allow the trained
machine learning models to spontaneously adjust to new vehicle domains. As
such, we propose the Recalibrated-Wheel Odometry neural Network (R-WhONet),
that adapts the WhONet model from its source domain (a vehicle and environment
on which the model is initially trained) to the target domain (a new vehicle on
which the trained model is to be deployed). Through a performance evaluation on
several GNSS outage scenarios - short-term complex driving scenarios, and on
longer-term GNSS outage scenarios. We demonstrate that a model trained in the
source domain does not generalise well to a new vehicle in the target domain.
However, we show that our new proposed framework improves the generalisation of
the WhONet model to new vehicles in the target domains by up to 32%.
