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Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

Defence Technology（防务技术） 2024年第3期33卷 475-493页

作者： Zeyu Li Changhui Jiang Xiaobo Gu Ying Xu Feng zhou Jianhui Cui College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao266590China geoloc laboratory Universite Gustave EiffelParis77454France School of Automation Guangdong University of TechnologyGuangzhou510006China

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other *** receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize *** its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian *** paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their *** paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation ***,the paper discusses the main challenges and open issues that require further research.

关键词： Collaborative positioning Vision LiDAR Wireless sensors Sensor fusion

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Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

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Defence Technology（防务技术） 2024年第3期33卷 573-587页

作者： Qu Wang Meixia Fu Jianquan Wang Lei Sun Rong Huang Xianda Li Zhuqing Jiang Yan Huang Changhui Jiang School of Automation Science and Electrical Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFo Shan528399China Research Institute of China Unicom Beijing100190China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China Department of Automation Tsinghua University Beijing100084China geoloc laboratory Universite Gustave EiffelParis77454France

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time ***,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with *** paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual *** at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent *** at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding *** at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude *** conduct extensive and well-designed experiments to evaluate the performance of the proposed *** experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

关键词： Indoor positioning Inertial navigation system(INS) Zero-velocity update(ZUPT) Internet of things(IoTs) Location-based service(LBS)

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