用方格靶标实现快速自动的激光雷达与相机标定
Improvement on LiDAR-Camera Calibration Using Square Targets
- 基于几何信息自动检测方格靶标，无需材料限制
- 抗初始误差和传感器噪声，标定结果稳定可靠
- 适合工厂生产与售后场景，部署简单效率高
精确的传感器标定对自动驾驶至关重要，1度的旋转误差在远距离目标检测中可导致米级位置偏差，引发系统误判甚至安全问题。尽管已有多种多传感器标定方法，但在工厂制造或售后场景中的实用性仍不足。本文提出一种基于方格靶标的全自动激光雷达-相机外参标定算法，具备快速、易部署、抗噪声等优点。核心包括：(1) 仅依赖几何信息的自动多阶段激光雷达板检测流程，无需特定材料；(2) 对初始外参误差鲁棒的快速粗略参数搜索机制；(3) 抗传感器噪声的直接优化算法。通过真实场景数据验证了方法的有效性。
原文摘要 · Abstract (English)
Precise sensor calibration is critical for autonomous vehicles as a prerequisite for perception algorithms to function properly. Rotation error of one degree can translate to position error of meters in target object detection at large distance, leading to improper reaction of the system or even safety related issues. Many methods for multi-sensor calibration have been proposed. However, there are very few work that comprehensively consider the challenges of the calibration procedure when applied to factory manufacturing pipeline or after-sales service scenarios. In this work, we introduce a fully automatic LiDAR-camera extrinsic calibration algorithm based on targets that is fast, easy to deploy and robust to sensor noises such as missing data. The core of the method include: (1) an automatic multi-stage LiDAR board detection pipeline using only geometry information with no specific material requirement; (2) a fast coarse extrinsic parameter search mechanism that is robust to initial extrinsic errors; (3) a direct optimization algorithm that is robust to sensor noises. We validate the effectiveness of our methods through experiments on data captured in real world scenarios.
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