  Achieving the highest levels of autonomy within autonomous vehicles (AVs)
requires a precise and dependable positioning solution that is not influenced
by the environment. 5G mmWave signals have been extensively studied in the
literature to provide such a positioning solution. Yet, it is evident that 5G
alone will not be able to provide uninterrupted positioning services, as
outages are inevitable to occur. Towards that end, few works have explored the
benefits of integrating mmWave positioning with onboard motion sensors (OBMS)
like inertial measurement units (IMUs) and odometers. Inspired by INS-GNSS
integration literature, all methods defaulted to a tightly-coupled (TC)
integration scheme, which hinders the potential of such an integration.
Additionally, the proposed methods were validated using simulated 5G and INS
data with probability-based line-of-sight (LOS) assumptions. Such an
experimental setup fails to highlight the true advantages and challenges of
5G-OBMS integration. Therefore, this study first explores a loosely-coupled
(LC) 5G-OBMS integration scheme as a viable alternative to TC schemes. Next, it
examines the merits and challenges of such an integration in a deep-urban
setting using a novel quasi-real simulation setup. The setup comprises
quasi-real 5G measurements from the Siradel simulator and real commercial-grade
IMU measurements from a challenging one-hour-long trajectory in downtown
Toronto. The trajectory featured multiple natural 5G outages which helped with
assessing the integration's performance. The proposed LC method achieved a
14-cm level of accuracy for 95% of the time, while significantly limiting
positioning errors during natural 5G outages.
