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Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments.

Safa Ouerghi1, Rémi Boutteau2, Xavier Savatier3

  • 1Carthage University, SUP'COM, GRESCOM, El Ghazela 2083, Tunisia. safa.ouerghi@supcom.tn.

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|March 23, 2018
PubMed
Summary
This summary is machine-generated.

This study enhances vehicle localization using visual odometry and SeqSLAM for precise urban navigation. Fusing these methods significantly reduces errors, making navigation more accurate and reliable.

Keywords:
EKFSeqSLAMUKFloop-closurereal-time navigationvisual-odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Vehicle localization in urban environments is challenging due to sensor drift.
  • Visual odometry tracks motion but accumulates errors over time.
  • Place recognition is crucial for correcting drift and enabling long-term navigation.

Purpose of the Study:

  • To develop and evaluate a robust vehicle localization system for urban environments.
  • To fuse visual odometry with SeqSLAM for improved long-term navigation accuracy.
  • To compare the performance of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for sensor fusion.

Main Methods:

  • Utilizing visual odometry for incremental motion estimation.
  • Implementing SeqSLAM for place recognition in challenging environments.
  • Fusing visual odometry and SeqSLAM using EKF and UKF for state estimation.
  • Online template library creation for SeqSLAM using visual odometry data.

Main Results:

  • The fused system achieved a significantly reduced overall position error of 0.22% compared to visual odometry alone (0.45%).
  • Loop-closure detection effectively reduced navigation errors.
  • EKF and UKF demonstrated comparable performance in this application.
  • EKF was identified as more computationally efficient than UKF for this fusion task.

Conclusions:

  • The fusion of visual odometry and SeqSLAM provides a superior solution for long-term vehicle localization in urban settings.
  • EKF is the preferred method for fusing visual odometry and SeqSLAM due to its efficiency.
  • The proposed method offers a reliable and accurate approach to autonomous navigation.