  Positional estimation is of great importance in the public safety sector.
Emergency responders such as fire fighters, medical rescue teams, and the
police will all benefit from a resilient positioning system to deliver safe and
effective emergency services. Unfortunately, satellite navigation (e.g., GPS)
offers limited coverage in indoor environments. It is also not possible to rely
on infrastructure based solutions. To this end, wearable sensor-aided
navigation techniques, such as those based on camera and Inertial Measurement
Units (IMU), have recently emerged recently as an accurate, infrastructure-free
solution. Together with an increase in the computational capabilities of mobile
devices, motion estimation can be performed in real-time. In this
demonstration, we present a real-time indoor positioning system which fuses
millimetre-wave (mmWave) radar and IMU data via deep sensor fusion. We employ
mmWave radar rather than an RGB camera as it provides better robustness to
visual degradation (e.g., smoke, darkness, etc.) while at the same time
requiring lower computational resources to enable runtime computation. We
implemented the sensor system on a handheld device and a mobile computer
running at 10 FPS to track a user inside an apartment. Good accuracy and
resilience were exhibited even in poorly illuminated scenes.
