摘要
为降低动态环境对视觉/惯性导航系统定位精度与稳定性的影响,提出了一种动态特征剔除的视觉/惯性导航方法。该方法在视觉/惯性导航系统VINS框架基础上,以结构相似度作为成本量生成端到端网络,检测环境中的动态区域;通过特征光流矢量对已检测到的动态区域进行对称光流筛选,剔除该区域内的动态特征;融合视觉和惯性测量构造代价函数,通过非线性优化方法有效估计无人系统状态。实验结果表明,动态特征剔除后的视觉/惯性导航方法具有良好的定位精度和稳定性,其位置均方根误差在EuRoC公开数据集和实际场景采集数据上分别为0.081和1.982 m,仅为VINS的35.5%和24.9%。该方法可在复杂应用环境中提供精确的位置信息,且在低成本无人系统导航定位方面具有良好的实用价值。
To reduce the impact of dynamic environment on the localization accuracy and stability of visual/inertial navigation system,a dynamic feature removal visual/inertial navigation method is proposed in this paper.Based on the VINS framework,this method uses structural similarity as the cost volume to generate an end-to-end network for dynamic regions detection.Symmetric optical flow screening is then performed on the identified dynamic regions to remove non-consistent outliers and further eliminate dynamic features that affect localization.The cost function is constructed by fusing visual and inertial measurements,and the nonlinear optimization method is used to estimate the unmanned system states effectively.The experimental results show that the visual/inertial navigation method with dynamic feature removal has good localization accuracy and stability,the position root mean square error is 0.081 and 1.982 m on EuRoC publicly available datasets and real scenario data respectively,which is only 35.5%and 24.9%of VINS.This method can provide accurate position information in complex application environment,and has good practical value in the navigation of low-cost unmanned systems.
作者
多靖赟
赵龙
赵毅琳
李俊韬
Duo Jingyun;Zhao Long;Zhao Yilin;Li Juntao(Beijing Key Laboratory of Intelligent Logistics System,Beijing Wuzi University,Beijing 101149,China;Digital Navigation Center,Beihang University,Beijing 100191,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第12期126-135,共10页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(42274037)
航空科学基金(2022Z022051001)
国家重点研发计划(2020YFB0505804)
北京市教委科技计划重点项目(KZ202210037046)
北京市通州区科技创新人才项目(JCQN2023030)
北京物资学院青年科研基金(2022XJQN22)项目资助。
关键词
视觉/惯性导航
动态特征剔除
结构相似度
端到端网络
对称光流
visual/inertial navigation
dynamic feature removal
structural similarity
end-to-end network
symmetric optical flow
