It is well known that the accuracy of camera calibration is constrained by the size of the reference plate,it is difficult to fabricate large reference plates with high precision.Therefore,it is non-trivial to calibra...It is well known that the accuracy of camera calibration is constrained by the size of the reference plate,it is difficult to fabricate large reference plates with high precision.Therefore,it is non-trivial to calibrate a camera with large field of view(FOV).In this paper,a method is proposed to construct a virtual large reference plate with high precision.Firstly,a high precision datum plane is constructed with a laser interferometer and one-dimensional air guideway,and then the reference plate is positioned at different locations and orientations in the FOV of the camera.The feature points of reference plate are projected to the datum plane to obtain a virtual large reference plate with high-precision.The camera is moved to several positions to get different virtual reference plates,and the camera is calibrated with the virtual reference plates.The experimental results show that the mean re-projection error of the camera calibrated with the proposed method is 0.062 pixels.The length of a scale bar with standard length of 959.778mm was measured with a vision system composed of two calibrated cameras,and the length measurement error is 0.389mm.展开更多
Imagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a de...Imagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a deliberately chosen but virtual camera trajectory. In this paper we present algorithms for the main sub-tasks (spatial calibration, image interpolation) related to this problem. Calibration: Spatial calibration of individual video streams is one of the most basic tasks related to creating such a video. At its core, this requires to estimate the pairwise relative geometry of images taken by different cameras. It is also known as the relative pose problem [1], and is fundamental to many computer vision algorithms. In practice, efficiency and robustness are of highest relevance for big data applications such as the ones addressed in the EU-FET_SME project SceneNet. In this paper, we present an improved algorithm that exploits additional data from inertial sensors, such as accelerometer, magnetometer or gyroscopes, which by now are available in most mobile phones. Experimental results on synthetic and real data demonstrate the accuracy and efficiency of our algorithm. Interpolation: Given the calibrated cameras, we present a second algorithm that generates novel synthetic images along a predefined specific camera trajectory. Each frame is produced from two “neighboring” video streams that are selected from the data base. The interpolation algorithm is then based on the point cloud reconstructed in the spatial calibration phase and iteratively projects triangular patches from the existing images into the new view. We present convincing images synthesized with the proposed algorithm.展开更多
Geometric error is the main factor affecting the machining accuracy of hybrid machine tools.Kinematic calibration is an effective way to improve the geometric accuracy of hybrid machine tools.The necessity to measure ...Geometric error is the main factor affecting the machining accuracy of hybrid machine tools.Kinematic calibration is an effective way to improve the geometric accuracy of hybrid machine tools.The necessity to measure both position and orientation at each pose,as well as the instability of identification in case of incomplete measurements,severely affects the application of traditional calibration methods.In this study,a kinematic calibration method with high measurement efficiency and robust identification is proposed to improve the kinematic accuracy of a five-axis hybrid machine tool.First,the configuration is introduced,and an error model is derived.Further,by investigating the mechanism error characteristics,a measurement scheme that only requires tool centre point position error measurement and one alignment operation is proposed.Subsequently,by analysing the effects of unmeasured degrees of freedom(DOFs)on other DOFs,an improved nonlinear least squares method based on virtual measurement values is proposed to achieve stable parameter identification in case of incomplete measurement,without introducing additional parameters.Finally,the proposed calibration method is verified through simulations and experiments.The proposed method can efficiently accomplish the kinematic calibration of the hybrid machine tool.The accuracy of the hybrid machine tool is significantly improved after calibration,satisfying actual aerospace machining requirements.展开更多
For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise i...For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.展开更多
文摘It is well known that the accuracy of camera calibration is constrained by the size of the reference plate,it is difficult to fabricate large reference plates with high precision.Therefore,it is non-trivial to calibrate a camera with large field of view(FOV).In this paper,a method is proposed to construct a virtual large reference plate with high precision.Firstly,a high precision datum plane is constructed with a laser interferometer and one-dimensional air guideway,and then the reference plate is positioned at different locations and orientations in the FOV of the camera.The feature points of reference plate are projected to the datum plane to obtain a virtual large reference plate with high-precision.The camera is moved to several positions to get different virtual reference plates,and the camera is calibrated with the virtual reference plates.The experimental results show that the mean re-projection error of the camera calibrated with the proposed method is 0.062 pixels.The length of a scale bar with standard length of 959.778mm was measured with a vision system composed of two calibrated cameras,and the length measurement error is 0.389mm.
文摘Imagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a deliberately chosen but virtual camera trajectory. In this paper we present algorithms for the main sub-tasks (spatial calibration, image interpolation) related to this problem. Calibration: Spatial calibration of individual video streams is one of the most basic tasks related to creating such a video. At its core, this requires to estimate the pairwise relative geometry of images taken by different cameras. It is also known as the relative pose problem [1], and is fundamental to many computer vision algorithms. In practice, efficiency and robustness are of highest relevance for big data applications such as the ones addressed in the EU-FET_SME project SceneNet. In this paper, we present an improved algorithm that exploits additional data from inertial sensors, such as accelerometer, magnetometer or gyroscopes, which by now are available in most mobile phones. Experimental results on synthetic and real data demonstrate the accuracy and efficiency of our algorithm. Interpolation: Given the calibrated cameras, we present a second algorithm that generates novel synthetic images along a predefined specific camera trajectory. Each frame is produced from two “neighboring” video streams that are selected from the data base. The interpolation algorithm is then based on the point cloud reconstructed in the spatial calibration phase and iteratively projects triangular patches from the existing images into the new view. We present convincing images synthesized with the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Nos.52275442 and 51975319)。
文摘Geometric error is the main factor affecting the machining accuracy of hybrid machine tools.Kinematic calibration is an effective way to improve the geometric accuracy of hybrid machine tools.The necessity to measure both position and orientation at each pose,as well as the instability of identification in case of incomplete measurements,severely affects the application of traditional calibration methods.In this study,a kinematic calibration method with high measurement efficiency and robust identification is proposed to improve the kinematic accuracy of a five-axis hybrid machine tool.First,the configuration is introduced,and an error model is derived.Further,by investigating the mechanism error characteristics,a measurement scheme that only requires tool centre point position error measurement and one alignment operation is proposed.Subsequently,by analysing the effects of unmeasured degrees of freedom(DOFs)on other DOFs,an improved nonlinear least squares method based on virtual measurement values is proposed to achieve stable parameter identification in case of incomplete measurement,without introducing additional parameters.Finally,the proposed calibration method is verified through simulations and experiments.The proposed method can efficiently accomplish the kinematic calibration of the hybrid machine tool.The accuracy of the hybrid machine tool is significantly improved after calibration,satisfying actual aerospace machining requirements.
基金supported by the National Natural Science Foundation of China (51906181)the 2021 Construction Technology Plan Project of Hubei Province (No.2021-83)the Excellent Young and Middle-aged Talent in Universities of Hubei Province,China (Q20181110).
文摘For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.
