  Odometer has been proven to significantly improve the accuracy of the Global
Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated
vehicle navigation in GNSS-challenged environments. However, the odometer is
inaccessible in many applications, especially for aftermarket devices. To apply
forward speed aiding without hardware wheeled odometer, we propose OdoNet, an
untethered one-dimensional Convolution Neural Network (CNN)-based
pseudo-odometer model learning from a single Inertial Measurement Unit (IMU),
which can act as an alternative to the wheeled odometer. Dedicated experiments
have been conducted to verify the feasibility and robustness of the OdoNet. The
results indicate that the IMU individuality, the vehicle loads, and the road
conditions have little impact on the robustness and precision of the OdoNet,
while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus,
a data-cleaning procedure is added to effectively mitigate the impacts of the
IMU biases and the mounting angles. Compared to the process using only
non-holonomic constraint (NHC), after employing the pseudo-odometer, the
positioning error is reduced by around 68%, while the percentage is around 74%
for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be
employed as an untethered pseudo-odometer for vehicle navigation, which can
efficiently improve the accuracy and reliability of the positioning in
GNSS-denied environments.
