A multiple-camera system calibration toolbox using a feature descriptor-based calibration pattern

This paper presents a novel feature descriptor-based calibration pattern and a Matlab toolbox which uses the specially designed pattern to easily calibrate both the intrin-sics and extrinsics of a multiple-camera system. In contrast to existing calibration patterns, in particular, the ubiquitous chessboard, the proposed pattern contains many more features of varying scales; such features can be easily and automatically detected. The proposed toolbox supports the calibration of a camera system which can comprise either normal pinhole cameras or catadioptric cameras. The calibration only requires that neighboring cameras observe parts of the calibration pattern at the same time; the observed parts may not overlap at all. No overlapping fields of view are assumed for the camera system. We show that the toolbox can easily be used to automatically calibrate camera systems.

Authors

• Bo Li 0018
• Lionel Heng
• Kevin Köser
• Marc Pollefeys

Keywords

• Cameras
• Calibration
• Mathematical model
• Feature extraction
• Equations
• Noise
• Robot vision systems
• System Calibration
• Calibration Pattern
• Calibration Toolbox
• Multiple Camera Systems
• Field Of View
• Camera System
• Pinhole Camera
• Feature Detection
• Feature Points
• Corresponding Points
• Image Distortion
• Image Point
• Model Projections
• Vertices
• Image Edge
• Intrinsic Parameters
• Camera Calibration
• Camera Model
• Camera Pose
• Global Coordinate System
• Pinhole Camera Model
• Stereo Camera
• Reprojection Error
• Fundamental Matrix
• Camera Coordinate System
• Fisheye Lens
• Visual Odometry
• Feature Matching
• Breadth-first Search
• Unified Model