摘要
极地复杂环境导致无人车在极地作业活动受限,因此需要联合无人机与无人车组成空地协同系统,高效完成作业。无人机-车协同定位是空地协同的基础,传统定位算法采用单一的视觉信息实现,定位精度受环境影响较大。而利用单一的惯性传感器定位易产生积分偏移,无法实现长时定位。基于此,基于扩展卡尔曼滤波(Extended Kalman Filter,EKF)器,提出一种无人机-车协同定位算法,利用Apriltag标识码结合PNP算法实现无人机对无人车的识别和视觉定位,融合无人车自身惯性传感器,实现无人机-车协同定位。实验结果表明,相较于单一的视觉定位算法和惯性导航定位算法,上述融合算法定位精度更高。
The complex polar environment limits the operational activities of unmanned vehicles in polar regions,therefore it is necessary to form an air ground coordination system by combining unmanned aerial vehicles and unmanned vehicles to efficiently complete operations.UAV vehicle collaborative positioning is the foundation of air ground collaboration.Traditional positioning algorithms use a single visual information to achieve positioning accuracy,which is greatly affected by the environment.On the other hand,the use of a single inertial sensor suffers from integral offset and cannot achieve long-time positioning.Based on this,this paper proposes an UAV-AGV cooperative positioning algorithm based on the Extended Kalman Filter(EKF).It uses the Apriltag identification code combined with the PNP algorithm to achieve the recognition and visual positioning for AGVs,and incorporates the UAV's own inertial sensors to achieve UAV-AGV cooperative positioning.The experimental results show that the proposed cooperative positioning algorithm has higher positioning accuracy compared with the single visual positioning algorithm and the inertial navigation positioning algorithm.
作者
樊家晖
窦银科
寇立伟
张宇
FAN Jia-hui;DOU Yin-ke;KOU Li-wei;ZHANG Yu(College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan Shanxi,030024,China;Shanxi Energy Internet Research Institute,Taiyuan Shanxi,030032,China;Department of Automation,Taiyuan Institute of Technology,Taiyuan Shanxi,030008,China)
出处
《计算机仿真》
2024年第10期254-259,共6页
Computer Simulation
基金
国家重点研发计划子课题(SQ2022YFC2800200-3)
山西省基础研究计划青年项目(202103021223048)
山西省研究生教育创新项目(2022Y223)。
关键词
协同定位
无人机
视觉定位
扩展卡尔曼滤波
数据融合
Cooperative positioning
Drones
Visual positioning
Extended Kalman filtering
Data fusion
