PseudoCal：迈向免初始化的基于深度学习的相机-激光雷达自标定
PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration
AI总结：
本文提出PseudoCal，一种基于伪激光雷达概念并在三维空间中工作的免初始化相机-激光雷达自标定方法，能够准独立于初始参数估计实现一次性标定，解决现有方法难以处理的极端标定场景。
摘要：
相机-激光雷达外参标定是自动驾驶车辆和移动机器人等自主系统中多传感器融合的关键任务。传统技术通常需要人工干预或特定环境，导致劳动密集且容易出错。现有的基于深度学习的自标定方法侧重于小幅重对齐，并且仍然依赖初始估计，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中工作，而不是局限于相机视场，从而克服了这些限制。在典型的自动驾驶车辆和机器人上下文与约定中，PseudoCal能够准独立于初始参数估计执行一次性标定，解决现有方法仍未解决的极端情况。
英文摘要：
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.
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