LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement
This addresses the challenge of enhancing spatial understanding in autonomous systems or mapping applications under poor lighting, though it appears incremental as it builds on existing fusion approaches.
This study tackled the problem of generating colorized LiDAR point clouds under low-light conditions by introducing a hardware-agnostic method using multi-camera fusion and low-light image enhancement, achieving real-time performance and reliable colorization that recovered scene details undetectable otherwise.
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.