arXiv 2506.025872025-06-03cs.CVcs.RO

BEVCALIB：通过几何引导的鸟瞰图表示进行LiDAR-相机标定

BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations

University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）

Weiduo Yuan, Jerry Li, Justin Yue, Divyank Shah, Konstantinos Karydis, Hang Qiu

发表信息：: 9th Conference on Robot Learning (CoRL 2025), Seoul, Korea

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AI总结：

本文提出首个利用鸟瞰图特征从原始数据执行LiDAR-相机标定的模型BEVCALIB，通过融合多模态BEV特征并引入特征选择器优化训练，在多个数据集上显著超越现有基线，确立了新的技术水准。

摘要：

准确的LiDAR-相机标定对于融合自动驾驶和机器人系统中的多模态感知至关重要。传统标定方法需要在受控环境中收集大量数据，且无法补偿车辆/机器人运动过程中的变换变化。本文提出了首个使用鸟瞰图（BEV）特征从原始数据执行LiDAR-相机标定的模型，称为BEVCALIB。为此，我们分别提取相机BEV特征和LiDAR BEV特征，并将其融合到共享的BEV特征空间中。为充分利用BEV特征中的几何信息，我们引入了一种新颖的特征选择器，以过滤变换解码器中最重要的特征，从而减少内存消耗并实现高效训练。在KITTI、NuScenes及我们自己的数据集上的广泛评估表明，BEVCALIB确立了新的技术水准。在各种噪声条件下，在KITTI数据集上，BEVCALIB在（平移，旋转）方面分别比文献中的最佳基线平均提高了（47.08%，82.32%），在NuScenes数据集上分别提高了（78.17%，68.29%）。在开源领域，它将最佳可复现基线的性能提升了一个数量级。代码和演示结果可在https://cisl.ucr.edu/BEVCalib获取。

英文摘要：

Accurate LiDAR-camera calibration is fundamental to fusing multi-modal perception in autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled environments and cannot compensate for the transformation changes during the vehicle/robot movement. In this paper, we propose the first model that uses bird's-eye view (BEV) features to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve this, we extract camera BEV features and LiDAR BEV features separately and fuse them into a shared BEV feature space. To fully utilize the geometric information from the BEV feature, we introduce a novel feature selector to filter the most important features in the transformation decoder, which reduces memory consumption and enables efficient training. Extensive evaluations on KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a new state of the art. Under various noise conditions, BEVCALIB outperforms the best baseline in the literature by an average of (47.08%, 82.32%) on KITTI dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation, rotation), respectively. In the open-source domain, it improves the best reproducible baseline by one order of magnitude. Our code and demo results are available at https://cisl.ucr.edu/BEVCalib.

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