视觉惯导紧耦合的行人室内自主定位方法Indoor autonomous positioning method with visual inertial tightly coupled for pedestrians
曾继超,许广富,刘锡祥
摘要(Abstract):
针对行人在大型复杂建筑环境中的高精度和高可靠性室内定位需求,传统的基于视觉点特征方法易受环境纹理缺失、相机快速运动导致图像模糊而定位失效问题,提出了一种基于视觉点线特征与IMU紧耦合的行人室内自主定位方法。在视觉惯导融合导航系统框架下,前端部分,在点特征基础上引入结构化建筑环境中丰富的线特征,并采取基于梯度密度过滤机制的改进线特征提取策略,剔除局部线特征密集区域;利用点线特征与IMU紧耦合优化机制提高行人位姿估计及定位的准确性和稳定性。通过利用EuRoC数据集和在实际楼道场景下的实验,特别是在弱纹理、光照变化等条件下实验,验证了所提方法进行行人室内定位的准确性和可行性。
关键词(KeyWords): 点线特征;IMU;梯度密度过滤机制;室内自主定位
基金项目(Foundation): 国家自然科学基金项目(61973079,51979041)
作者(Author): 曾继超,许广富,刘锡祥
DOI: 10.16251/j.cnki.1009-2307.2021.07.004
参考文献(References):
- [1] 裴凌,刘东辉,钱久超.室内定位技术与应用综述[J].导航定位与授时,2017,4(3):1-10.(PEI Ling,LIU Donghui,QIAN Jiuchao.A survey of indoor positioning technology and application[J].Navigation Positioning and Timing,2017,4(3):1-10.)
- [2] 邓中亮,尹露,唐诗浩,等.室内定位关键技术综述[J].导航定位与授时,2018,5(3):14-23.(DENG Zhongliang,YIN Lu,TANG Shihao,et al.A survey of key technology for indoor positioning[J].Navigation Positioning and Timing,2018,5(3):14-23.)
- [3] 刘浩敏,章国锋,鲍虎军.基于单目视觉的同时定位与地图构建方法综述[J].计算机辅助设计与图形学学报,2016,28(6):855-868.(LIU Haomin,ZHANG Guofeng,BAO Hujun.A survey of monocular simultaneous localization and mapping[J].Journal of Computer-Aided Design & Computer Graphics,2016,28(6):855-868.)
- [4] 徐爱功,宋帅,隋心,等.一种单目视觉/INS组合室内定位抗差方法[J].测绘科学,2019,44(12):1-6.(XU Aigong,SONG Shuai,SUI Xin,et al.A single ege vision/INS combination indoor positioning robust method[J].Science of Surveying and Mapping,2019,44(12):1-6.)
- [5] 邹汉达,袁洪.视觉辅助下的室内惯导位姿修正[J].测绘通报,2019(5):16-20.(ZOU Handa,YUAN Hong.Pose correction of INS for indoor location based on a vision navigation method[J].Bulletin of Surveying and Mapping,2019(5):16-20.)
- [6] 王丹,黄鲁,李垚.基于点线特征的单目视觉同时定位与地图构建算法[J].机器人,2019,41(3):392-403.(WANG Dan,HUANG Lu,LI Yao.A monocular visual SLAM algorithm based on point-line feature[J].Robot,2019,41(3):392-403.)
- [7] 孟庆瑜.基于点线特征的单目视觉-惯性里程计算法研究[D].吉林:东北电力大学,2019.(MENG Qingyu.Research on monocular vision-inertial odometry algorithm based on points and lines features[D].Jilin,China:Northeast Dianli University,2019.)
- [8] ZOU Danping,WU Yuanxin,PEI Ling,et al.StructVIO:visual-inertial odometry with structural regularity of man-made environments[J].IEEE Transactions on Robotics,2019,35(4):999-1013.
- [9] ZUO Xingxing,XIE Xiaojia,LIU Yong,et al.Robust visual SLAM with point and line features[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).Vancouver,BC,Canada:IEEE,2017:1775-1782.
- [10] GROMPONE VON GIOI R,JAKUBOWICZ J,MOREL J M,et al.LSD:A fast line segment detector with a false detection control[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(4):722-732.
- [11] 高翔,张涛,刘毅.视觉SLAM十四讲:从理论到实践[M].北京:电子工业出版社,2017.(GAO Xiang,ZHANG Tao,LIU Yi.Fourteen lectures on visual SLAM:from theory to practice[M].Beijing:Publishing House of Electronics Industry,2017.)
- [12] BARTOLI A,STURM P.The 3D line motion matrix and alignment of line reconstructions[J].International Journal of Computer Vision,2004,57(3):159-178.
- [13] SOLà J.Quaternion kinematics for the error-state Kalman filter[EB/OL].[2020-02-19].http://www.iri.upc.edu/people/jsola/publications/kinematics.pdf.
- [14] HE Yijia,ZHAO Ji,GUO Yue,et al.PL-VIO:Tightly-coupled monocular visual-inertial odometry using point and line features[J].Sensors:Basel,Switzerland,2018,18(4):E1159.
- [15] QIN Tong,LI Peiliang,SHEN Shaojie.VINS-mono:A robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics,2018,34(4):1004-1020.
- [16] SALAüN Y,MARLET R,MONASSE P.Multiscale line segment detector for robust and accurate SfM[C]//2016 23rd International Conference on Pattern Recognition(ICPR).Cancun:IEEE,2016:2000-2005.
- [17] BURRI M,NIKOLIC J,GOHL P,et al.The EuRoC micro aerial vehicle datasets[J].The International Journal of Robotics Research,2016,35(10):1157-1163.