摘要
为了实现消防救援、反恐作战等应急任务复杂场景下行人的室内外无缝定位,提出了一种基于位姿图优化的综合利用微惯性导航系统和全球卫星导航系统的融合定位方法。首先利用经典的EKF滤波算法实现微惯导和卫导融合定位,结合卫导长期精度较高、误差稳定和不随时间累积的优点以及微惯导短时精度高、输出连续、自主定位的优点,实现行人在复杂环境中的室内外无缝定位。在此基础上,提出了一种图优化结合EKF的融合定位新框架,利用卫导定位终端和足部微惯导定位模块进行了实验,比较EKF、图优化-EKF两种算法的融合定位效果。实验结果显示,面对室内外复杂环境,单纯的EKF算法融合定位轨迹航向偏差较大,图优化-EKF算法在轨迹方向保持性上表现更佳,相比EKF算法其融合定位轨迹航向误差减小了48.87%,位姿图优化-EKF算法具有更好的全局稳定性和航向精准度,显著降低了卫星测量异常值对融合定位结果的影响。
In order to achieve indoor and outdoor seamless positioning of pedestrians in complex scenes of fire rescue,anti-terrorism operations and other emergency tasks,a fusion positioning method based on pose graph optimization is proposed,which integrates micro inertial navigation system and global navigation satellite system.Firstly,the classical EKF filtering algorithm is used to realize the integration of micro inertial navigation and satellite navigation,combined with the advantages of high long-term precision,stable error and non accumulation over time of satellite navigation,as well as the advantages of high short-term precision,continuous output and autonomous positioning of micro inertial navigation,the indoor and outdoor seamless positioning of pedestrians in complex environment is realized.On this basis,a new fusion positioning framework based on graph optimization and EKF is proposed.Experiments are carried out by using satellite positioning terminal and foot micro inertial navigation positioning module to compare the fusion positioning effects of EKF and graph optimization-EKF.The experimental results show that,in the face of indoor and outdoor complex environment,the pure EKF algorithm has a large deviation in the fusion positioning trajectory heading,and the graph optimization-EKF algorithm performs better in the trajectory direction maintenance.Compared with EKF algorithm,the fusion positioning trajectory heading error of graph optimization-EKF algorithm is reduced by 48.87%,and has better global stability and heading accuracy,significantly reduces the impact of satellite measurement outliers on the fusion positioning results.
作者
闵涛
罗小勇
高峰
李杨寰
吴学忠
MIN Tao;LUO Xiaoyong;GAO Feng;LI Yanghuan;WU Xuezhong(Institute of Intelligent Science,National University of Defense Technology,Changsha 410073,China;Hunan Glonavin Information Technology Co.,Ltd.,Changsha 410205,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2021年第1期40-47,共8页
Journal of Chinese Inertial Technology
基金
国家重点研发计划(2018YFB2002300)
国家自然科学基金(52075540)。