CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth
This addresses the problem of aligning camera and LiDAR data for autonomous driving and robotics, offering a simpler, adaptive solution that is incremental over existing methods.
The paper tackles camera-LiDAR calibration by proposing CLAIM, a method that uses monodepth models and a coarse-to-fine search to minimize structure and texture losses, achieving superior performance on KITTI, Waymo, and MIAS-LCEC datasets compared to state-of-the-art methods.
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.