Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything (2306.02656v1)

Abstract: The research on extrinsic calibration between Light Detection and Ranging(LiDAR) and camera are being promoted to a more accurate, automatic and generic manner. Since deep learning has been employed in calibration, the restrictions on the scene are greatly reduced. However, data driven method has the drawback of low transfer-ability. It cannot adapt to dataset variations unless additional training is taken. With the advent of foundation model, this problem can be significantly mitigated. By using the Segment Anything Model(SAM), we propose a novel LiDAR-camera calibration method, which requires zero extra training and adapts to common scenes. With an initial guess, we opimize the extrinsic parameter by maximizing the consistency of points that are projected inside each image mask. The consistency includes three properties of the point cloud: the intensity, normal vector and categories derived from some segmentation methods. The experiments on different dataset have demonstrated the generality and comparable accuracy of our method. The code is available at https://github.com/OpenCalib/CalibAnything.

• The paper introduces a zero-training calibration method leveraging SAM for detailed image segmentation and consistent point cloud processing.
• It employs a two-stage extrinsic optimization, first using brute-force rotation search then refined random search for fine-tuning rotation and translation.
• Evaluations on datasets like KITTI show improved accuracy with a 10.7 cm translation error and 0.174° rotation error compared to existing methods.

An Analysis of "Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything"

The paper "Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything" presents an innovative approach to the challenge of extrinsic calibration between LiDAR and cameras, crucial for applications like autonomous driving. This method leverages the Segment Anything Model (SAM), a foundational model for image segmentation, to perform calibration without requiring additional training on target datasets, thereby significantly enhancing adaptability and applicability across various scenarios.

Methodology

The core contribution of this paper is a novel calibration method which requires no additional training and exploits SAM for image segmentation coupled with point cloud consistency. The method is bifurcated into several key stages:

1. Image Segmentation: Utilizing SAM, the method segments entire images to yield detailed masks of differentiated objects. The segmentation is fine-tuned to be granular, aiding in capturing the intricacies necessary for effective calibration.
2. Point Cloud Processing: The process involves normal estimation via eigenvalue analysis of a covariance matrix formed from neighboring points, intensity normalization, and segmentation through techniques like plane fitting and clustering.
3. Extrinsic Optimization: The optimization focuses on maximizing consistency in reflectivity, normal vectors, and segmentation class across projected points within segmented image masks. A scoring function evaluates the alignment quality, and an extrinsic parameter search is implemented in two stages: initial brute-force search for rotation optimization and refined random search for fine-tuning both rotation and translation parameters.

Results

The paper presents qualitative and quantitative evaluations using datasets such as KITTI and an in-house dataset. The method outperforms existing alternatives in terms of L2 and Huber loss metrics, demonstrating superiority in both translational and rotational accuracy. On the KITTI dataset, the proposed method achieves mean vector norm errors of 10.7 cm in translation and 0.174 degrees in rotation, underscoring its efficacy and precision.

Implications and Future Work

The research details a promising approach for LiDAR-camera calibration, offering significant practical implications due to its zero-training versatility. This adaptability facilitates its application across varied environments without being impeded by dataset-specific constraints, thus broadening the practical deployment horizon of calibration methods in autonomous systems.

From a theoretical perspective, the integration of foundation models like SAM indicates a shift towards leveraging pre-trained capabilities for higher-order applications in machine learning and computer vision. This paradigm could pave the way for more generalized, robust, and adaptive solutions in multi-sensor fusion disciplines.

Future directions hinted at in the paper include expanding experiments across a broader spectrum of datasets, conducting comparative analyses against existing state-of-the-art methods, and utilizing stability metrics as additional evaluation dimensions. These expansions will fortify the validation of this approach and could potentially unearth further optimizations to enhance calibration fidelity.

In conclusion, this research underscores the potential of SAM as a robust tool for sensor calibration, offering a significant step forward in overcoming the traditional limitations associated with data-driven calibration methods. Such advancements signify meaningful progress in the domain of autonomous sensing and navigation technologies.