弱光环境下基于图像增强的视觉惯性定位方法OA
Visual-inertial localization method based on image enhancement under low-light environments
针对智能车辆、机器人等移动装备中的传统视觉惯性定位方法在弱光环境下存在的位姿漂移和定位失败等问题,提出了一种以VINs-Mono算法为框架结合图像增强技术的视觉惯性定位方法.在VINs-Mono算法前端,首先对相机输入的图像数据进行多尺度Retinex图像增强处理,然后依次进行限制对比度的直方图均衡化和设计自适应矫正因子的光度矫正处理,并基于处理结果的平均灰度值和熵值进行加权融合.最后使用轮式机器人采集数据,进行了轨迹精度对比试验.结果表明:相比无图像增强的方案和改进前的图像增强方案,所提出方法的轨迹跟踪均方根误差平均分别降低了22.74％和8.57％,新方法可以有效提升弱光环境下视觉惯性导航系统的定位精度.
To solve the problems of pose drift and localization failures by traditional visual-inertial methods in mobile systems of intelligent vehicles and robots under low-light conditions,the novel visual-inertial localization method was developed by incorporating image enhancement techniques into the VINS-Mono framework.In the front end of VINS-Mono algorithm,the input image stream was processed with multi-scale Retinex enhancement.The contrast-limited adaptive histogram equalization and the photometric correction module featuring with adaptive correction factor were applied.The weighted fusion strategy based on the average grayscale value and entropy of the processed images was employed to integrate the results.Using wheeled robot for data collection,the trajectory accuracy comparison experiment was conducted.The results show that compared to the scheme without image enhancement and the scheme with baseline enhancement,the proposed method reduces the root mean square error of trajectory tracking by the average of 22.74％and 8.57％,respectively.The proposed approach significantly improves the localization accuracy of visual-inertial navigation systems under low-light environments.
江浩斌;傅世友;李傲雪;任俊豪;刘光耀
江苏大学汽车工程研究院,江苏镇江 212013江苏大学汽车与交通工程学院,江苏镇江 212013江苏大学汽车与交通工程学院,江苏镇江 212013江苏大学汽车与交通工程学院,江苏镇江 212013江苏大学汽车与交通工程学院,江苏镇江 212013
信息技术与安全科学
弱光环境视觉惯性定位特征提取图像增强VINS-Mono自适应矫正多尺度高斯函数
low-light environmentvisual-inertial localizationfeature extractionimage enhancementVINS-Monoadaptive correctionmulti-scale Gaussian function
《江苏大学学报（自然科学版）》 2026 (3)
316-322,7
江苏省产业前瞻与共性关键技术竞争项目(BE2017129)
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