Indoor positioning system in visually-degraded environments with millimetre-wave radar and inertial sensors: demo abstract

Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 18th ACM Conference on Embedded Networked Sensor Systems
EditorsShunsuke Saruwatari
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages623-624
Number of pages2
ISBN (Electronic)9781450375900
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventACM Conference on Embedded Networked Sensor Systems 2020 - Online, Japan
Duration: 16 Nov 202019 Nov 2020
Conference number: 18th
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3384419 (Proceedings)

Conference

ConferenceACM Conference on Embedded Networked Sensor Systems 2020
Abbreviated titleSenSys 2020
Country/TerritoryJapan
Period16/11/2019/11/20
Internet address

Keywords

  • deep learning
  • IMU
  • indoor positioning
  • millimeter-wave sensor

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