用可动光源主动照亮纹理区，显著提升黑暗中的运动估计精度。
Active Illumination for Visual Ego-Motion Estimation in the Dark
- 通过深度网络识别关键纹理区域，动态控制光源精准照射
- 实测使姿态估计误差降低75%，优于传统固定照明方案
- 适合需夜间或低光环境导航的机器人系统
视觉里程计（VO）和视觉即时定位与地图构建（V-SLAM）系统在低光和黑暗环境中常因缺乏鲁棒视觉特征而表现不佳。本文提出一种新型主动照明框架，以增强此类条件下的算法性能。该方法通过动态控制移动光源，照射高纹理区域，从而改善特征提取与跟踪。具体而言，一个融合深度学习增强网络的检测模块用于识别具有相关特征的区域；随后，云台控制器将光束引导至这些区域，为运动估计算法提供信息丰富的图像。在真实机器人平台上的实验结果表明，该方法相较于传统固定照明技术，姿态估计误差最多降低75%。
原文摘要 · Abstract (English)
Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。