Deep Odometry Systems on Edge with EKF-LoRa Backend for Real-Time Indoor Positioning

Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Vu Tran, L. N. S. Wijayasingha, M. Arif Rahman, John A. Stankovic, Andrew Markham, Niki Trigoni

Research output: Chapter in Book/Published conference outputConference publication


Ubiquitous positioning for pedestrians in adverse environments has been a long standing challenge. Despite dramatic progress made by Deep Learning, multi-sensor deep odometry systems still 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 select and compare three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a millimetre-wave (mmWave) radar, and a thermal infrared camera, and implement their deep odometry inference engines to run in real-time. A pipeline for deploying deep odometry on edge platforms with different resource constraints is proposed. We design a LoRa link for positional data backhaul and project aggregated positions of deep odometry into the global frame. We find that a simple EKF backend is sufficient for generic odometry 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.
Original languageEnglish
Title of host publication2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER)
Number of pages6
Publication statusPublished - 3 May 2022

Bibliographical note

Copyright. This research has been financially supported by the National Institute of Standards and Technology (NIST) via the grant Pervasive, Accurate, and Reliable Location-based Services for Emergency Responders (Federal Grant: 70NANB17H185).
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