用鸟瞰图对齐实现激光雷达与相机高精度标定
CalibBEV: LiDAR-Camera Calibration via BEV Alignment
- 将激光雷达和相机数据统一到共享的鸟瞰图特征空间
- 在KITTI和nuScenes上相对旋转误差降低51%~68%
- 结合对比学习与显式对齐，适合自动驾驶多传感器系统
我们提出CalibBEV，一种基于鸟瞰图（BEV）对齐的新型激光雷达-相机标定方法。该方法将两种模态数据统一到共享的3D空间表示中，实现准确且鲁棒的跨模态标定。CalibBEV使用领域特定架构从各传感器提取贝叶斯特征，并通过两步对齐过程估计标定矩阵：首先，直接从BEV特征回归粗略标定矩阵；为缓解对齐难度，引入受CLIP启发的对比损失，强制跨模态BEV表示语义一致，引导网络进入统一特征空间。第二步，利用BEV表述显式对齐一模态特征与另一模态，将初始粗估计精炼为更精确的最终标定矩阵。CalibBEV显著优于先前点对像素匹配方法，在KITTI和nuScenes基准上分别将相对旋转误差（RRE）降低51%和68%，相对平移误差（RTE）降低80%和91%。
原文摘要 · Abstract (English)
We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation, enabling accurate and robust cross-modal calibration. CalibBEV extracts sensor-wise BEV features from each modality using domain-specific architectures and estimates the calibration matrix through a two-step alignment process. First, we perform an implicit alignment by regressing a coarse calibration matrix directly from the BEV features. To ease this alignment, we enforce semantic consistency between BEV representations across modalities using a contrastive loss inspired by CLIP, guiding both networks toward a unified feature space. In the second step, we leverage our BEV formulation to explicitly align the features of one modality with the other, refining the initial coarse estimate into a final, more accurate calibration matrix. CalibBEV significantly outperforms prior point-to-pixel matching methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, our method reduces the Relative Rotation Error (RRE) by 51% and 68%, and the Relative Translation Error (RTE) by 80% and 91%, respectively, compared to previous methods.
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