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3-D rigid body tracking using vision and depth sensors.

O Serdar Gedik, A Aydn Alatan

IEEE Transactions on Cybernetics

|August 20, 2013

Summary

This study introduces an automated 3-D object tracking algorithm for robotics and augmented reality. The novel method fuses vision and depth sensors for accurate, jitter-free pose estimation without manual initialization or offline training.

Area of Science:

Robotics
Computer Vision
Augmented Reality

Background:

Accurate 3-D pose estimation is crucial for robotics and augmented reality (AR).
Existing vision-based trackers often require manual initialization or offline training.
Pure depth sensor trackers are unsuitable for AR applications.

Purpose of the Study:

To propose an automated 3-D tracking algorithm for rigid objects.
To enhance tracking reliability and reduce jitter in AR and robotics.
To develop a method that eliminates the need for manual initialization or offline training.

Main Methods:

Fusion of vision and depth sensors using an extended Kalman filter.
A novel measurement-tracking scheme utilizing optical flow estimation.

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Estimation of optical flow from intensity and shape index map data of 3-D point clouds.

Main Results:

Significantly improved 2-D and 3-D tracking performance.
Demonstrated superior accuracy compared to conventional techniques via error metrics.
Subjective evaluation of rendered scenes confirmed high-quality tracking.

Conclusions:

The proposed algorithm enables highly accurate 3-D tracking without manual initialization or offline training.
Sensor fusion approach enhances robustness and applicability in AR and robotics.
The method offers a significant advancement in automated 3-D object tracking.