摘要
为提高自动导引车(automated guided vehicle,AGV)在复杂视觉环境下的定位性能、降低硬件成本,提出了一种基于AprilTag和模糊互补滤波的视觉惯性里程计(visual-Inertial Odometry,VIO)。采用扩展卡尔曼滤波器(extended Kalman filter,EKF)融合陀螺仪、磁力计和编码器测量数据,计算航向角用于航位推算。通过对相机AprilTag识别距离和运动速度进行模糊推算获取标识权重,加权计算AprilTag进行视觉定位,减小多标识视觉定位误差。通过标识权重均值计算互补融合系数,将视觉定位和航位推算结果互补融合,提高VIO定位精度。实验结果表明,所提出的VIO在小型AGV的定位精度达到了41.84 mm,比惯性里程计和传统卡尔曼滤波的AprilTag-VIO分别提高了52.20%和20.75%。
In order to improve the positioning performance of AGV(automated guided vehicle)in complex visual environment and reduce the hardware cost,VIO(visual inertia odometer)based on AprilTag and fuzzy complementary filter was proposed.EKF(extended Kalman filter)was used to integrate the measurement data of gyroscope,magnetometer and encoder to calculate the course angle,which is used for dead reckoning.Fuzzy calculations were used to determine tag weight based on recognition distance and motion speed of camera.The weighted sum of AprilTags was used for visual positioning to reduce multi-tag positioning error.The complementary fusion coefficients were derived from the mean value of tag weight.Finally,the visual positioning and the dead reckoning results were fused by complementary coefficients to improve the VIO positioning accuracy.Experimental results show that the proposed VIO implemented on AGV has a 41.84 mm positioning accuracy,which is 52.20%and 20.75%higher than that of dead-reckoning and AprilTag-VIO with traditional Kalman filter.
作者
刘艳
王卓
LIU Yan;WANG Zhuo(Information Engineering College,Dalian University,Dalian 116622,China;Environmental Perception and Intelligent Control Key Laboratory,Dalian University,Dalian 116622,China)
出处
《科学技术与工程》
北大核心
2024年第30期13048-13054,共7页
Science Technology and Engineering
基金
辽宁省教育厅科学计划研究项目(L2019607)
大连市科技创新基金计划(2020JJ26SN058)。
