Authors:
Yingna Su
1
;
2
;
Xinnian Guo
1
;
2
and
Yang Shen
3
Affiliations:
1
College of Information Engineering, Suqian University, Suqian, China
;
2
Suqian Key Laboratory of Visual Inspection and Intelligent Control, Suqian University, Suqian, China
;
3
Industrial Technology Research Institute, Suqian University, Suqian, China
Keyword(s):
Camera Self-Calibration, Gravity Direction, Homography Constraints, Principal Point Estimation.
Abstract:
Camera calibration is crucial for enabling accurate and robust visual perception. This paper addresses the challenge of recovering intrinsic camera parameters from two views of a planar surface, that has received limited attention due to its inherent degeneracy. For cameras equipped with Inertial Measurement Units (IMUs), such as those in smartphones and drones, the camera’s y-axes can be aligned with the gravity direction, reducing the relative orientation to a one-degree-of-freedom (1-DoF). A key insight is the general orthogonality between the ground plane and the gravity direction. Leveraging this ground plane constraint, the paper introduces new homography-based minimal solutions for camera self-calibration with a known gravity direction. we derive 2.5- and 3.5-point camera self-calibration algorithms for points in the ground plane to enable simultaneous estimation of the camera’s focal length and principal point. The paper demonstrates the practicality and efficiency of these a
lgorithms and comparisons to existing state-of-the-art methods, confirming their reliability under various levels of noise and different camera configurations.
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