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Advanced Monocular Outdoor Pose Estimation in Autonomous Systems: Leveraging Optical Flow, Depth Estimation, and

Alireza Ghasemieh1, Rasha Kashef1

  • 1Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive visual odometry framework using monocular cameras for GPS-independent localization in autonomous vehicles. It enhances safety and navigation in complex environments, offering a cost-effective solution.

Keywords:
depth estimationoptical flowpose estimationsemantic segmentationvisual odometry

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Artificial Intelligence

Background:

  • Global Positioning System (GPS) limitations in obstructed environments necessitate GPS-independent localization for autonomous vehicles (AVs).
  • Existing solutions using LiDAR or stereo cameras are often costly and complex.
  • Monocular vision offers a practical, cost-effective alternative but lacks robust pose estimation models.

Purpose of the Study:

  • To develop a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry (VO) with monocular cameras.
  • To address the need for robust, GPS-independent localization solutions adaptable to various platforms and cost constraints.
  • To ensure safety and real-time decision-making for AVs in GPS-denied areas.

Main Methods:

  • Utilized visual odometry (VO) to estimate camera pose from image sequences in GPS-denied environments.
  • Developed an adaptive framework leveraging monocular vision, advanced control theory, and machine learning.
  • Integrated AI-driven models to meet multi-sensor system performance standards.

Main Results:

  • Achieved significant improvements in pose estimation accuracy on the KITTI odometry dataset.
  • Demonstrated a cost-effective and robust solution for real-world AV applications.
  • Enhanced AV safety and performance in complex traffic scenarios through integrated control theory.

Conclusions:

  • The proposed adaptive visual odometry framework provides a reliable and cost-effective GPS-independent localization solution for autonomous vehicles.
  • This approach enhances navigation safety and adaptability in challenging environments where traditional GPS is unavailable.
  • The research advances monocular vision capabilities for autonomous systems, paving the way for wider adoption.