兵工自动化2026，Vol.45Issue(4)：107-112,6.DOI:10.7690/bgzdh.2026.04.020
室内光照变化下视觉SLAM优化方法
Optimization Methods for Visual SLAM under Indoor Illumination Variations
摘要
Abstract
Aiming at the problems such as low robustness caused by the changes of indoor lighting conditions in traditional visual simultaneous localization and mapping(SLAM),an optimized visual SLAM method based on image enhancement and neural network is proposed.This method makes improvements on the original ORB-SLAM2 framework by incorporating a RAH-GCNv2 feature point extraction method into its camera tracking thread.The RAH-GCNv2 method performes equalization processing on the RGB channels of the image to adjust the visual information color bias phenomenon,and conductes adaptive enhancement on the HSV channels of the image to adjust the brightness issue.Through the GCNv2 feature point extraction network,uniformly distributed and scattered feature points were obtained,and experimental verification was carried out on public datasets.The experimental results showed that under underexposure and overexposure conditions,the proposed improved method increased the standard deviation of the captured images by 5 times,the entropy value by 50%,and the average gradient of the images by 5 times.After integrating the RAH-GCNv2 feature point extraction method into the ORB-SLAM2 framework,the camera motion trajectory error was reduced by 30%compared with the original ORB-SLAM2 framework,and problems such as pose loss did not occur.The actual test showed that the trajectory drift problem of the original ORB-SLAM2 framework in weakly textured scenes was corrected,and the mapping effect was significantly improved.关键词
视觉SLAM/移动机器人/图像增强/特征点提取Key words
visual SLAM/mobile robot/image enhancement/feature point extraction分类
信息技术与安全科学引用本文复制引用
肖鑫,黄丹平,李昊宇..室内光照变化下视觉SLAM优化方法[J].兵工自动化,2026,45(4):107-112,6.基金项目
四川省科技厅项目(2024YFFK0220) （2024YFFK0220）
