layout: farshid_default title: “Camera Calibration & Video Stabilization” description: “Springer book chapter on fuzzy camera calibration and video stabilization for robot localization.” markmap: | # Camera Calibration & Video Stabilization ## Framework

• Fuzzy Camera Calibration (FCC)
• Fuzzy Optical Flow (FOF) ## Experiments
• DARWIN-OP humanoid robot
• Stereo vision localization ## Results
• 15% lower error vs Zhang’s method
• Applicable to AR and autonomous vehicles tags: [camera-calibration, video-stabilization, robotics, slam] hashtags: “#cameracalibration #videostabilization #robotics #slam”

My_Book_chapter_Camera_Calibration_and_Video_Stabilization_Framework_for_Robot_Localization in the Book entitled “Control Engineering in Robotics and Industrial Automation” published in Springer

https://www.pirahansiah.com/contents/publications/Books/My_Book_chapter_Camera_Calibration_and_Video_Stabilization_Framework_for_Robot_Localization/

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Camera Calibration and Video Stabilization Framework for Robot Localization

1. Introduction

• Key Issues in Localization: Camera calibration (CC) and video stabilization (VS).
• Problems in Camera Calibration:
  • Current methods use fixed thresholds, neglecting slope information, leading to blurring.
  • Gaussian pyramid parameters in optical flow require manual tuning.
• Challenges in Robot Vision:
  • Large motion, motion blur, and defocus blur.
  • Landmark recognition and probabilistic models fail due to image distortion.
• Proposed Solution: A framework using Fuzzy Camera Calibration (FCC) and Fuzzy Optical Flow (FOF) for video stabilization.

2. Overview of Robot Localization

• Simultaneous Localization and Mapping (SLAM):
  • Central focus in robotics.
  • Provides real-time mapping and robot localization.
• Applications of SLAM:
  • Robotics competitions (e.g., RoboCup).
  • Autonomous vehicles and unmanned aerial systems.

3. Robot Localization Approaches

3.1 Multi-Sensor Fusion

• Laser Rangefinder:
  • Accurate but faces issues with glass and reflective surfaces.
  • Ultrasonic beacons improve accuracy but are more error-prone.
• Sensor Networks:
  • Combines multiple sensors for improved accuracy.
  • Sensors include ultrasonic, depth cameras, and lasers.

3.2 Vision Localization

• Model-Based:
  • Uses edges, lines, and contours for comparison with object models.
  • Challenges with occlusion and universal models.
• Appearance-Based:
  • Utilizes local features like Harris corners and SIFT for object recognition and tracking.

4. Proposed Framework for Robot Localization

• Fuzzy Camera Calibration (FCC):
  • Provides intrinsic and extrinsic camera parameters.
  • Adjusts for camera distortions and motion blur.
• Fuzzy Optical Flow (FOF):
  • Stabilizes video frames for improved localization.
  • Adaptive parameters based on image quality and motion.

5. Experimental Setup

• Humanoid Robot Platform:
  • Uses DARWIN-OP humanoid robot equipped with stereo cameras.
  • Cameras mounted for stereo vision and depth detection in RoboCup environments.
• Camera Calibration Experiment:
  • Calibration using chessboard pattern with 9×6 points.
  • Tests conducted with different blurriness and distances.

6. Dataset and Evaluation

• Dataset Structure:
  • 700 images (350 pairs) with varying angles, blur, and noise.
  • Tests conducted using Mono vision and Stereo vision methods.
• Ground Truth Measurements:
  • Distance measurement for robot localization using stereo vision.
  • Comparison with ground truth and Mono vision methods.

7. Results

• Comparison with Mono Vision:
  • FCC and FOF methods show significantly improved distance accuracy.
  • Errors reduced in distance measurements for various landmark positions.
• Stereo Vision Performance:
  • Stereo vision outperforms Mono vision in accuracy.
  • Fewer errors in robot localization even when landmarks are partially visible.

8. Proposed Framework for Robot Localization

• Framework Steps:
  • Step 1: Apply FCC for camera calibration.
  • Step 2: Use FOF for video stabilization.
  • Step 3: Measure distance and localize landmarks.
• Stereo Vision Method:
  • Un-distortion, rectification, correspondence, and re-projection for distance measurement.

9. Comparisons with Other Methods

• Comparison with Zhang’s Method:
  • Proposed FCC shows 15% lower error in distance measurements.
  • FCC has 12% lower standard deviation than Zhang’s method.
• Fuzzy Gaussian Pyramid for Video Stabilization:
  • Dynamic adjustment of parameters based on image sharpness and motion.
  • Outperforms fixed-parameter methods in handling large motions.

10. Conclusion

• Framework Effectiveness:
  • FCC and FOF improve robot localization and distance measurements.
  • Stereo vision system enhances localization accuracy in dynamic environments.
• Broader Applications:
  • Proposed methods applicable in augmented reality, autonomous vehicles, and 3D mapping.