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Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera.

Alwin Poulose1, Dong Seog Han1

  • 1School of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea.

Sensors (Basel, Switzerland)
|November 27, 2019
PubMed
Summary
This summary is machine-generated.

Hybrid indoor localization systems combining smartphone cameras and inertial measurement unit (IMU) sensors significantly improve positioning accuracy. These integrated systems overcome limitations of individual camera or IMU approaches, offering more reliable indoor navigation.

Keywords:
ArUco markersIMU sensorsKalman filterheading estimationindoor navigationindoor positioning system (IPS)pedestrian dead reckoning (PDR)sensor fusionsimultaneous localization and mapping (SLAM)smartphone camera

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Area of Science:

  • Robotics and Computer Vision
  • Sensor Fusion for Localization

Background:

  • Smartphone cameras and Inertial Measurement Unit (IMU) sensors offer independent indoor positioning capabilities.
  • IMU-based systems face accuracy issues due to sensor errors like electromagnetic noise and drifts.
  • Camera-based systems are limited by reliance on floor maps, camera poses, and rapid directional changes.

Purpose of the Study:

  • To develop and evaluate hybrid indoor localization systems that integrate smartphone camera and IMU sensor data.
  • To minimize positioning errors inherent in standalone camera or IMU localization methods.
  • To analyze the performance of hybrid systems compared to individual approaches.

Main Methods:

  • Designed an indoor experiment to assess IMU-based, camera-based, and hybrid localization systems.
  • Implemented hybrid systems by fusing camera-based techniques (ORB-SLAM, UcoSLAM) with IMU sensor data.
  • Evaluated system performance using metrics like average localization error, mean error, maximum error, and standard deviation.

Main Results:

  • The proposed hybrid systems demonstrated superior position accuracy compared to individual IMU and camera-based systems.
  • The ORB-SLAM with IMU hybrid system achieved a mean localization error of 0.1398 m.
  • The UcoSLAM with IMU hybrid system achieved a lower mean localization error of 0.0690 m.

Conclusions:

  • Hybrid systems combining camera and IMU sensors are effective for enhancing indoor localization accuracy.
  • The UcoSLAM with IMU hybrid system showed particularly strong performance in the conducted experiments.
  • Sensor fusion offers a robust solution to mitigate individual sensor limitations in indoor positioning.