CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth

TL;DR

CLAIM introduces a simple, adaptive camera-LiDAR calibration method leveraging monodepth models, using correlation and mutual information metrics to achieve superior alignment without complex preprocessing.

Contribution

The paper presents CLAIM, a novel calibration approach that uses a coarse-to-fine search with correlation and mutual information losses, avoiding complex feature extraction.

Findings

Outperforms state-of-the-art methods on KITTI, Waymo, and MIAS-LCEC datasets.

Requires no complicated data processing or feature matching.

Demonstrates robustness across diverse scenes.

Abstract

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.