Abstract
In recent years, the fusion of camera data with LiDAR measurements has emerged as a powerful approach to enhance spatial understanding. This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs, providing complete 360-degree coverage. The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module within the fusion pipeline. The system requires initial calibration to determine intrinsic camera parameters, followed by automatic computation of the geometric transformation between the LiDAR and cameras, removing the need for specialised calibration targets and streamlining the setup. The data processing framework uses colour correction to ensure uniformity across camera feeds before fusion. The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration. The optimised software achieved real-time performance and reliable colourisation even under very low illumination, successfully recovering scene details that would otherwise remain undetectable.
Abstract (translated)
近年来，相机数据与激光雷达（LiDAR）测量的融合已成为增强空间理解力的一种强大方法。本研究介绍了一种新颖且硬件无关的方法，利用多台摄像机输入为机械激光雷达生成彩色点云，并提供完整的360度覆盖范围。该方法的主要创新在于其在低光条件下表现出的强大鲁棒性，通过在数据融合管道中集成一个低光图像增强模块实现。 系统需要进行初始校准以确定相机的内在参数，随后自动计算激光雷达与摄像机之间的几何变换，这消除了对特殊校准目标的需求，并简化了设置流程。该数据处理框架采用颜色校正技术，在点云融合前确保来自各摄像头的数据具有一致性。 算法使用Velodyne Puck Hi-Res LiDAR和四相机配置进行了测试。优化后的软件实现了实时性能，并在极低光照条件下也能实现可靠的彩色化，成功地恢复了原本可能无法检测到的场景细节。
URL
https://arxiv.org/abs/2509.25859
