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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (4): 711-717    DOI: 10.3785/j.issn.1008-973X.2022.04.010
计算机技术、信息工程     
基于改进MSCKF的无人机室内定位方法
王思鹏(),杜昌平*(),宋广华,郑耀
浙江大学 航空航天学院，浙江 杭州 310027
Indoor positioning method of UAV based on improved MSCKF algorithm
Si-peng WANG(),Chang-ping DU*(),Guang-hua SONG,Yao ZHENG
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
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摘要：

针对无人机室内定位容易出现漂移的问题，提出基于改进多状态约束卡尔曼滤波器（MSCKF）的无人机（UAV）室内定位方法. 该方法在MSCKF的框架下，提出高鲁棒性、低时延的标志点检测方法. 利用在世界坐标系下坐标已知的标志点计算得到无人机位姿，实现惯性测量单元(IMU)信息与单目视觉信息融合以及无人机位姿修正. 对提出的定位方法进行测试. 测试结果表明，该方法的定位误差小于0.266 m，与OpenVins和LARVIO开源算法相比，定位精度提高了54.6%以上.
关键词： 无人机(UAV);  室内定位;  卡尔曼滤波器;  标志点检测;  位姿修正    
Abstract:

An indoor positioning method of unmanned aerial vehicle (UAV) based on improved multi-state constraint Kalman filter (MSCKF) was proposed aiming at the problem that the indoor positioning of UAV is prone to drift. A high robustness and low delay detection method was proposed under the framework of MSCKF. The pose of UAV was calculated with the help of the known positions of the mark points in world coordinate system. Then inertial measurement unit (IMU) data and monocular vision data fusion and UAV pose correction were realized. The proposed positioning method was tested. The simulation results show that the positioning error of the proposed method was within 0.266 m, and the positioning accuracy was improved by more than 54.6% compared to OpenVins and LARVIO.
Key words: unmanned aerial vehicle (UAV)    indoor positioning    Kalman filter    mark detection    pose correction
收稿日期: 2021-05-20 出版日期: 2022-04-24
CLC:  TP 242  
基金资助: 装备预研教育部联合基金资助项目(6141A02011803)
通讯作者: 杜昌平     E-mail: wangsipeng@zju.edu.cn;duchangping@zju.edu.cn
作者简介: 王思鹏（1997—），男，硕士生，从事飞行器路径规划与定位的研究. orcid.org/0000-0003-4387-0279. E-mail： wangsipeng@zju.edu.cn
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引用本文:

王思鹏,杜昌平,宋广华,郑耀. 基于改进MSCKF的无人机室内定位方法[J]. 浙江大学学报(工学版), 2022, 56(4): 711-717.

Si-peng WANG,Chang-ping DU,Guang-hua SONG,Yao ZHENG. Indoor positioning method of UAV based on improved MSCKF algorithm. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 711-717.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.04.010    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I4/711

图 1  定位算法的整体框图
图 2  标志点检测流程
图 3  定位算法的性能测试环境
图 4  轨迹1与轨迹2的定位图
图 5  路径1的误差比较
算法 误差/m
轨迹1 轨迹2
本文算法 0.226 0.266
OpenVins 0.498 0.644
LARVIO 0.575 —
表 1  2条轨迹下3种算法的定位误差
图 6  路径2的误差比较
算法 td/ms
本文算法 7.92
OpenVins 2.67
文献[13]算法 >22.22
表 2  各定位算法的实时性分析
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