Automatic Surround Camera Calibration Method in Road Scene for Self-driving Car

TL;DR

This paper presents a robust automatic calibration method for surround-view cameras in autonomous vehicles, using a coarse-to-fine random-search strategy to improve accuracy and robustness over traditional methods.

Contribution

Introduces a novel coarse-to-fine random-search calibration method that effectively handles large initial errors in multi-camera setups for autonomous driving.

Findings

Achieves high calibration accuracy in real and simulated environments.

Demonstrates robustness against large initial extrinsic parameter errors.

Provides an open-source implementation for practical use.

Abstract

With the development of autonomous driving technology, sensor calibration has become a key technology to achieve accurate perception fusion and localization. Accurate calibration of the sensors ensures that each sensor can function properly and accurate information aggregation can be achieved. Among them, camera calibration based on surround view has received extensive attention. In autonomous driving applications, the calibration accuracy of the camera can directly affect the accuracy of perception and depth estimation. For online calibration of surround-view cameras, traditional feature extraction-based methods will suffer from strong distortion when the initial extrinsic parameters error is large, making these methods less robust and inaccurate. More existing methods use the sparse direct method to calibrate multi-cameras, which can ensure both accuracy and real-time performance and is theoretically achievable. However, this method requires a better initial value, and the initial estimate with a large error is often stuck in a local optimum. To this end, we introduce a robust automatic multi-cameras (pinhole or fisheye cameras) calibration and refinement method in the road scene. We utilize the coarse-to-fine random-search strategy, and it can solve large disturbances of initial extrinsic parameters, which can make up for falling into optimal local value in nonlinear optimization methods. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robustness performance. The open-source code is available at https://github.com/OpenCalib/SurroundCameraCalib.