Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are...Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.Findings–A lab PFR with the bar-laser-perception device is developed and tested in the field,and the results indicate that the proposed models perform well in normal person-following scenarios.Originality/value–This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.展开更多
This paper proposes a person-following method based on monocular vision,which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions.Our method ...This paper proposes a person-following method based on monocular vision,which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions.Our method is composed of a person detector,a Kalman filter(KF)tracker,and a re-identification module.To be more specific,the person detector uses a human pose estimation method to detect persons.The KF is applied to predict the position of the target person and update its state with detection results.A re-identification module is proposed to deal with distractions,where the Convolutional Channel Features(CCF)is used to extract appearance features and Online Boosting is used to distinguish the target person from others.Especially,we design a target recapture mechanism based on the Recurrent Neural Network(RNN).Combining motion information with appearance features,the system can accurately re-identify the target person.Without extra customized markers,our method can track the target person steadily in real-time only using a monocular camera.Experiments results can validate the robustness and effectiveness of the proposed method.展开更多
基金supported by the Basal Research Fund of Central Public Research Institute of China(Grant No.20212702).
文摘Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.Findings–A lab PFR with the bar-laser-perception device is developed and tested in the field,and the results indicate that the proposed models perform well in normal person-following scenarios.Originality/value–This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.
基金This paper was supported in part by the National Science Founda-tion of China(Grant No.62103237).
文摘This paper proposes a person-following method based on monocular vision,which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions.Our method is composed of a person detector,a Kalman filter(KF)tracker,and a re-identification module.To be more specific,the person detector uses a human pose estimation method to detect persons.The KF is applied to predict the position of the target person and update its state with detection results.A re-identification module is proposed to deal with distractions,where the Convolutional Channel Features(CCF)is used to extract appearance features and Online Boosting is used to distinguish the target person from others.Especially,we design a target recapture mechanism based on the Recurrent Neural Network(RNN).Combining motion information with appearance features,the system can accurately re-identify the target person.Without extra customized markers,our method can track the target person steadily in real-time only using a monocular camera.Experiments results can validate the robustness and effectiveness of the proposed method.
