Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly dist...Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.展开更多
The mung bean variety Ji Heilv No.12 was bred by Institute of Characteristic Crop Research, Chongqing Academy of Agricultural Sciences and Institute of Food and Oil Crops, Hebei Academy of Agricultural and Forestry Sc...The mung bean variety Ji Heilv No.12 was bred by Institute of Characteristic Crop Research, Chongqing Academy of Agricultural Sciences and Institute of Food and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences. using Jilv 9and Jilv 7 as female and male parent respectively,with pedigree method. Ji Heilv No.12 is a new variety with features of high and stable yield,broad adaptability and strong resistance in yield trails during 2011-2012; it was approved and released by Chongqing Provincial Committee of Crop Variety Identification in 2012,suitable for cultivating in most area of Chongqing.展开更多
A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori infor-mation of the system is proposed to model the DC-DC converters.A parsimonious model with fewer...A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori infor-mation of the system is proposed to model the DC-DC converters.A parsimonious model with fewer parameters is used to represent the system,and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data.Compared with traditional data-driven methods for DC-DC convert-ers,the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity.Moreover,compared with the traditional full-parameter constrained subspace approach,the proposed algorithm can accurately estimate the system parameters with a smaller variance.The experimental results on a DC-DC synchronous buck converter verify the effectiveness and superiority of the proposed method.展开更多
基金Supported by Tianjin Municipal Natural Science Foundation of China(Grant No.19JCJQJC61600)Hebei Provincial Natural Science Foundation of China(Grant Nos.F2020202051,F2020202053).
文摘Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.
基金Supported by Chongqing Science&Technology Commission(cstc2016shmszx80116csct2012jj A80042+5 种基金cstc2013yykfc800022015cstc-jbky-005072015cstc-jbky-00508)National Modern Agricultural Industry Technology System(CARS-09)Chongqing Finance Program(NKY-2016AB009NKY-2016AA002)
文摘The mung bean variety Ji Heilv No.12 was bred by Institute of Characteristic Crop Research, Chongqing Academy of Agricultural Sciences and Institute of Food and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences. using Jilv 9and Jilv 7 as female and male parent respectively,with pedigree method. Ji Heilv No.12 is a new variety with features of high and stable yield,broad adaptability and strong resistance in yield trails during 2011-2012; it was approved and released by Chongqing Provincial Committee of Crop Variety Identification in 2012,suitable for cultivating in most area of Chongqing.
基金supported in part by the Chongqing Natural Science Foundation(Nos.CSTB2022NSCQ-MSX1225,cstc2021jcyj-msxmX0142)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission(Nos.KJQN202000602,KJQN202200626)+2 种基金in part by the National Natural Science Foundation of China(No.61903057)in part by the China Postdoctoral Science Foundation(No.2022MD713688)in part by the Chongqing Postdoctoral Science Foundation(No.2021XM3079).
文摘A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori infor-mation of the system is proposed to model the DC-DC converters.A parsimonious model with fewer parameters is used to represent the system,and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data.Compared with traditional data-driven methods for DC-DC convert-ers,the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity.Moreover,compared with the traditional full-parameter constrained subspace approach,the proposed algorithm can accurately estimate the system parameters with a smaller variance.The experimental results on a DC-DC synchronous buck converter verify the effectiveness and superiority of the proposed method.