Automatic Surround Camera Calibration Method in Road Scene for Self-driving Car (2305.16840v1)

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.

• The paper introduces an automatic, targetless calibration method by leveraging photometric errors in BEV images to enhance sensor accuracy in diverse road scenes.
• The methodology employs a robust coarse-to-fine random-search strategy to overcome significant extrinsic parameter disturbances and avoid local optima.
• The open-source implementation provided by the authors facilitates community engagement and further research in autonomous vehicle calibration.

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

The paper presented by Li et al. addresses the challenging problem of automatic surround camera calibration in the domain of autonomous driving. Specifically, it focuses on enhancing the accuracy and robustness of camera systems in self-driving cars by improving the calibration of surround-view cameras. This paper is pivotal as the precision in sensor calibration significantly influences perception and depth estimation capabilities of autonomous vehicles.

Summary of Key Contributions

This work introduces a novel method aimed at calibrating both pinhole and fisheye cameras in road scenes. The method employs a coarse-to-fine random-search strategy, designed to address significant initial extrinsic parameter disturbances. This technique mitigates the typical pitfalls of feature extraction-based methods, such as distortion, and circumvents the issue of local optima in nonlinear optimization procedures. The key contributions of the work include:

1. Automatic, Targetless Calibration: The proposed method leverages photometric errors in the overlapping regions of adjacent camera images transformed into a bird’s eye view (BEV). This allows calibration without relying on specific target features, which is particularly beneficial in diverse and dynamic driving environments.
2. Robust Coarse-to-Fine Strategy: Through a multi-phase random-search approach, the method adapts to significant initial errors in extrinsic camera parameters. This strategy enhances the robustness of the calibration process, ensuring accurate and seamless synthetic views such as BEV images from various cameras.
3. Open-Source Implementation: An open-source implementation is provided to facilitate community engagement and further research. The authors have made their code available on GitHub, which underscores their commitment to transparency and collaboration in advancing the field of autonomous vehicle technology.

Methodological Insights

The proposed solution focuses on the critical problem of aligning multiple camera perspectives by minimizing photometric loss across images from different cameras. The technique is underpinned by several methodological design choices:

• Photometric Loss-Based Calibration: By focusing on photometric consistency across images, the method sidesteps the need for distinct geometric feature correspondences or lane markings, which are common constraints in traditional methods.
• Random-Search Optimization: This approach provides a balance between exhaustive searching and gradient-based optimization, offering robustness against local minima challenges typical in high-dimensional parameter spaces.

Implications and Future Work

The implications of this research are manifold. Practically, the method can significantly streamline the process of camera calibration in autonomous vehicles, directly enhancing the reliability of perception systems. Theoretically, it opens new avenues for research in sensor fusion and calibration techniques that do not depend on fixed features or patterns.

Future directions outlined in the paper suggest improving the real-time performance of this algorithm and enhancing its efficacy in environments with limited textural details. This could potentially extend the applicability of the technique to a wider array of autonomous system configurations and operational scenarios.

Overall, Li et al. present a compelling method that robustly addresses key challenges in sensor calibration for self-driving cars, positioning it as a valuable tool for researchers and practitioners in autonomous vehicle technology.