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Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld

Nicolas Antigny1,2,3, Hideaki Uchiyama4, Myriam Servières5,6,7

  • 1Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR) AME GEOLOC, 44340 Bouguenais, France. nicolas.antigny@ifsttar.fr.

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Summary

This study introduces a new method for accurate 3D pose estimation using mobile devices in urban settings. Adaptive step length estimation significantly improves positioning accuracy for pedestrian navigation.

Keywords:
augmented realityhandheld devicelocalizationpedestrian navigationpose estimationurban mobility

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Area of Science:

  • Computer Vision
  • Robotics
  • Geospatial Information Science

Background:

  • Urban environments pose significant challenges for handheld device pose estimation due to large displacements.
  • Low-cost sensors (monocular camera, IMU) on mobile devices limit accuracy in pedestrian navigation.

Purpose of the Study:

  • To develop a continuous pose estimation framework for handheld devices in urban environments.
  • To address scale ambiguity and drift in monocular Visual Odometry using adaptive methods.

Main Methods:

  • Proposed a continuous pose estimation system based on monocular Visual Odometry.
  • Implemented adaptive pedestrian step length estimation for horizontal plane displacements.
  • Utilized a handheld equipment height model with Digital Terrain Models for vertical axis estimation.
  • Incorporated object recognition for punctual pose correction and Visual Odometry reset.

Main Results:

  • Achieved a positioning error of 1.6–7.5% of the walked distance over a 0.7 km urban path.
  • Demonstrated the superiority of adaptive step length estimation over fixed-step length methods.
  • Validated the framework's effectiveness with experimental data from various pedestrians.

Conclusions:

  • The proposed framework significantly enhances pose estimation accuracy for mobile devices in challenging urban settings.
  • Adaptive step length estimation is crucial for improving pedestrian navigation precision.
  • The integration of GIS data and object recognition offers robust pose correction capabilities.