用单目深度模型提升相机与激光雷达的对齐精度。
CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth
- 基于粗到精搜索，优化相机与激光雷达间变换矩阵。
- 在KITTI等数据集上优于现有方法，无需复杂预处理。
- 适合自动驾驶场景，对不同环境适应性强。
本文挖掘单目深度模型在相机-激光雷达标定中的潜力，提出一种名为CLAIM的新方法，实现相机与激光雷达数据的对齐。给定初始估计及图像与点云配对数据，CLAIM采用粗到精搜索策略，通过最小化基于局部皮尔逊相关性的结构损失和基于互信息的纹理损失，寻找最优变换。这两种损失作为对齐质量的评估指标，无需复杂的数据处理、特征提取或匹配步骤，使方法简单且适用于多数场景。我们在公开的KITTI、Waymo和MIAS-LCEC数据集上验证了CLAIM，实验结果表明其性能显著优于现有最先进方法。代码已开源：https://github.com/Tompson11/claim。
原文摘要 · Abstract (English)
In this paper, we unleash the potential of the powerful monodepth model in camera-LiDAR calibration and propose CLAIM, a novel method of aligning data from the camera and LiDAR. Given the initial guess and pairs of images and LiDAR point clouds, CLAIM utilizes a coarse-to-fine searching method to find the optimal transformation minimizing a patched Pearson correlation-based structure loss and a mutual information-based texture loss. These two losses serve as good metrics for camera-LiDAR alignment results and require no complicated steps of data processing, feature extraction, or feature matching like most methods, rendering our method simple and adaptive to most scenes. We validate CLAIM on public KITTI, Waymo, and MIAS-LCEC datasets, and the experimental results demonstrate its superior performance compared with the state-of-the-art methods. The code is available at https://github.com/Tompson11/claim.
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