用多摄像头融合与低光增强，给激光点云自动上色
LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement
- 多相机融合+低光增强，实现全天候点云着色
- 实测在极低光照下仍能恢复可见细节
- 无需特殊标定物，支持真实场景快速部署
近年来，将相机数据与激光雷达（LiDAR）测量值融合已成为提升空间感知能力的有效方法。本研究提出一种新型、硬件无关的点云着色方法，利用多个摄像头输入生成机械式激光雷达的彩色点云，实现360度完整覆盖。核心创新在于其在低光条件下的鲁棒性，通过在融合流程中集成低光图像增强模块实现。系统需初始校准以确定相机内参，随后自动计算激光雷达与相机间的几何变换，无需专用标定靶，简化了部署流程。数据处理框架采用色彩校正确保各相机画面一致性。算法基于Velodyne Puck Hi-Res激光雷达与四相机配置进行测试，优化后的软件实现实时性能，在极低光照下仍能可靠完成点云着色，并成功还原原本不可见的场景细节。
原文摘要 · Abstract (English)
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.
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