Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems, such as self-driving vehicles and mobile robots. Traditional techniques often require manual intervention or specific environments, making them labour-intensive and error-prone. Existing deep learning-based self-calibration methods focus on small realignments and still rely on initial estimates, limiting their practicality. In this paper, we present PseudoCal, a novel self-calibration method that overcomes these limitations by leveraging the pseudo-LiDAR concept and working directly in the 3D space instead of limiting itself to the camera field of view. In typical autonomous vehicle and robotics contexts and conventions, PseudoCal is able to perform one-shot calibration quasi-independently of initial parameter estimates, addressing extreme cases that remain unsolved by existing approaches.

翻译：相机-激光雷达外参标定是自动驾驶车辆与移动机器人等自主系统中多传感器融合的关键任务。传统方法通常需要人工干预或特定环境，导致劳动密集且易出错。现有基于深度学习的自标定方法专注于小范围重对齐，仍依赖初始估计值，限制了其实用性。本文提出PseudoCal，一种新颖的自标定方法，通过利用伪激光雷达概念并直接在三维空间（而非局限于相机视野）中工作，克服了上述局限。在典型自动驾驶车辆与机器人场景及惯例下，PseudoCal能够近乎独立于初始参数估计完成单次标定，解决了现有方法尚未处理的极端情况。