4 CONCLUSION
This paper proposes a self-calibration method for
estimating camera focal length and principal point
based on the orthogonality assumption and homog-
raphy constraints. Leveraging IMU data and the or-
thogonality assumption, new homography constraints
are derived in this paper. The 2.5-point and 3.5-point
methods for estimating camera focal length and prin-
cipal point are presented. Thanks to the simpliﬁed
constraints, the algorithm in this paper not only ex-
hibits superior performance compared to alternative
approaches but also ensures high efﬁciency. We be-
lieve that the method proposed in this paper can serve
as an alternative algorithm for camera self-calibration
in intelligent vehicle applications, further enhancing
the performance of intelligent vehicle systems.
ACKNOWLEDGEMENTS
The authors would like to thank the editor and
the anonymous reviewers for their critical and con-
structive comments and suggestions. This work is
supported by Suqian science and technology plan
project under No. K202233, K202229, K202231,
H202117 and Suqian Natural Science Foundation
(No. M202305).
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