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Seamless Indoor-Outdoor Switching Localization Algorithm based on CKF

Journal of Communications and Information Networks 2024年第1期9卷 88-98页

作者： Gang Yang Guodong Hou Ningning Feng Weida Meng School of Communication and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China

To solve the problem of large positioning error and discontinuous positioning of special forces members when moving in cross-region indoors and outdoors, and to compensate for the linearization error and local convergence problem that may exist in the extended kalman filter(EKF)in nonlinear systems, that is,the iterative results may be trapped in a local optimum situation, a seamless indoor-outdoor switching localization algorithm based on cubature kalman filter (CKF) is proposed. CKF does not require the computation of Jacobian matrices, which can improve computational efficiency and filtering accuracy to a certain extent. In the system, an inertial measurement unit (IMU) is employed to correct the positioning errors of ultra-wideband (UWB)and BeiDou navigation satellite system(BDS).The positioning data from UWB and BeiDou in cross-region are weighted fused and then fused with the data from the IMU using CKF to obtain the final accurate positioning information. This study designs a scene-switching mechanism to achieve seamless switching between indoor and outdoor positioning scenes. By jointly analyzing the positioning accuracy of UWB and BeiDou,the positioning scene is determined, and appropriate counting thresholds are set to avoid frequent erroneous scene switches. Experimental results show that the proposed algorithm achieves a positioning accuracy of approximately 21.7 cm in cross-region,which can enable seamless integration of indoor and outdoor positioning,avoid positioning jumps, and enhance positioning accuracy.

关键词： cubature kalman filtering ultra-wideband inertial measurement unit fusion positioning seamless switching

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Cooperative Localization in Unknown Environment towards Multi-Robot Systems

Cooperative Localization in Unknown Environment towards Mult...

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第43届中国控制会议

作者： Xiaohang Yang Lelai Zhou Chen Zhang Xincheng Tian Yibin Li School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education

Localization plays an important role in robotics, aiming at the problem of multi-robot cooperative localization in a GPS-denied environment, a multi-sensor fusion cooperative localization method based on relative observations is presented in this paper. The relative observation module is comprised primarily of a monocular camera and an ultra-wideband(UWB),which associates visual detection with UWB distance measurement data to obtain relative measurement models for multi-robot systems. An Extended cubature kalman Filter(ECKF) is designed to fuse observations and internal sensor odometry data to improve system accuracy and robustness. The algorithm deploys on a single robot, with a fleet of sharing relative *** evaluate the performance of the proposed method, we conduct the experiment in the Gazebo simulation environment and the cooperative localization experiment is carried out on three unmanned robots in the real world. Comparing the global camera position as the ground truth, the results show the accuracy and robustness of our system.

关键词： Multi-Robot Systems Relative Localization Cooperative Localization GPS-denied environment cubature kalman filtering

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Maximum Likelihood Criterion based Adaptive CKF Algorithm for Integrated Navigation

Maximum Likelihood Criterion based Adaptive CKF Algorithm fo...

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第十一届中国卫星导航年会

作者： Song Li Ming Ma Xiaomei Tang Yangbo Huang Feixue Wang College of Electronic Science National University of Defense Technology

cubature kalman filtering(CKF) is a very effective nonlinear filtering algorithm,and has been successfully applied in integrated navigation ***,the algorithm still has several shortcomings

关键词： integrated navigation cubature kalman filtering singular value decomposition maximum likelihood criterion

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