  Ubiquitous positioning for pedestrian in adverse environment has served a
long standing challenge. Despite dramatic progress made by Deep Learning,
multi-sensor deep odometry systems yet pose a high computational cost and
suffer from cumulative drifting errors over time. Thanks to the increasing
computational power of edge devices, we propose a novel ubiquitous positioning
solution by integrating state-of-the-art deep odometry models on edge with an
EKF (Extended Kalman Filter)-LoRa backend. We carefully compare and select
three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a
millimetre-wave (mmWave) radar, and a thermal infrared camera, and realise
their deep odometry inference engines which runs in real-time. A pipeline of
deploying deep odometry considering accuracy, complexity, and edge platform is
proposed. We design a LoRa link for positional data backhaul and projecting
aggregated positions of deep odometry into the global frame. We find that a
simple EKF based fusion module is sufficient for generic positioning
calibration with over 34% accuracy gains against any standalone deep odometry
system. Extensive tests in different environments validate the efficiency and
efficacy of our proposed positioning system.
