Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and globa...Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and global map cannot be previously known. As a result, ICP algorithm will take much amount of iterations to reach convergence. On the other hand,establishment of correspondence is done by global searching, which requires enormous computational time. To overcome the two problems,a fast ICP-SLAM with rough alignment and narrowing-scale nearby searching is proposed. As for the decrease of iterative times,rough alignment based on initial pose matrix is proposed. In detail,initial pose matrix is obtained by micro-electro-mechanical system( MEMS) magnetometer and global landmarks. Then rough alignment will be applied between current scan frame and global map at the beginning of ICP algorithm with initial pose matrix. As for accelerating the establishment of correspondence, narrowingscale nearby searching with dynamic threshold is proposed,where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM,the experimental results show that the amount of iteration for ICP algorithm to reach convergence reduces to 92. 34% and ICP algorithm runtime reduces to 98. 86% on average. In addition,computational cost is kept in a stable level due to the eliminating of the accumulation of computational consumption. Moreover,great improvement can also been achieved in SLAM quality and robustness.展开更多
To reduce the uncertainty and reworks in complex projects,a novel mechanism is systematically developed in this paper based on two classical design structure matrix(DSM)clustering methods:Loop searching method(LSM)and...To reduce the uncertainty and reworks in complex projects,a novel mechanism is systematically developed in this paper based on two classical design structure matrix(DSM)clustering methods:Loop searching method(LSM)and function searching method(FSM).Specifically,the optimal working areas for the two clustering methods are first obtained quantitatively in terms of non-zero fraction(NZF)and singular value modularity index(SMI),in which the whole working area is divided into six sub-zones.Then,a judgement procedure is proposed for conveniently choosing the optimal DSM clustering method,which makes it easy to determine which DSM clustering method performs better for a given case.Subsequently,a conceptual model is constructed to assist project managers in effectively analyzing the network of projects and greatly reducing reworks in complex projects by defining preventive actions.Finally,the aircraft design process is presented to show how the proposed judgement mechanism can be utilized to reduce the reworks in actual projects.展开更多
A searching-machining system of RL & SM (Rapid Location and State Memory) universal fixture is here introduced, and the concept of rapid searching technology and manufacturing information transformation is then de...A searching-machining system of RL & SM (Rapid Location and State Memory) universal fixture is here introduced, and the concept of rapid searching technology and manufacturing information transformation is then described, with the appropriate control methods and key techniques for its realization being proposed and also practically realized. Theoretical analysis and experimental results show that the proposed idea and methods are feasible to serve as in the practical application of the RL & SM system.展开更多
An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT...An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations. In this paper, we show that it is the same as the Nemirovski’s approach, and then modify it to obtain an accelerate Nemirovski’s technique and prove the convergence. Our preliminary computational results are very favorable.展开更多
文摘Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and global map cannot be previously known. As a result, ICP algorithm will take much amount of iterations to reach convergence. On the other hand,establishment of correspondence is done by global searching, which requires enormous computational time. To overcome the two problems,a fast ICP-SLAM with rough alignment and narrowing-scale nearby searching is proposed. As for the decrease of iterative times,rough alignment based on initial pose matrix is proposed. In detail,initial pose matrix is obtained by micro-electro-mechanical system( MEMS) magnetometer and global landmarks. Then rough alignment will be applied between current scan frame and global map at the beginning of ICP algorithm with initial pose matrix. As for accelerating the establishment of correspondence, narrowingscale nearby searching with dynamic threshold is proposed,where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM,the experimental results show that the amount of iteration for ICP algorithm to reach convergence reduces to 92. 34% and ICP algorithm runtime reduces to 98. 86% on average. In addition,computational cost is kept in a stable level due to the eliminating of the accumulation of computational consumption. Moreover,great improvement can also been achieved in SLAM quality and robustness.
基金supported by the National Natural Science Foundation of China (Nos. 71471087, 71071076, 61673209)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics (No. BCXJ17-11)the Research and Innovation Program for Graduate Education of Jiangsu Province (No. KYZZ160145)
文摘To reduce the uncertainty and reworks in complex projects,a novel mechanism is systematically developed in this paper based on two classical design structure matrix(DSM)clustering methods:Loop searching method(LSM)and function searching method(FSM).Specifically,the optimal working areas for the two clustering methods are first obtained quantitatively in terms of non-zero fraction(NZF)and singular value modularity index(SMI),in which the whole working area is divided into six sub-zones.Then,a judgement procedure is proposed for conveniently choosing the optimal DSM clustering method,which makes it easy to determine which DSM clustering method performs better for a given case.Subsequently,a conceptual model is constructed to assist project managers in effectively analyzing the network of projects and greatly reducing reworks in complex projects by defining preventive actions.Finally,the aircraft design process is presented to show how the proposed judgement mechanism can be utilized to reduce the reworks in actual projects.
文摘A searching-machining system of RL & SM (Rapid Location and State Memory) universal fixture is here introduced, and the concept of rapid searching technology and manufacturing information transformation is then described, with the appropriate control methods and key techniques for its realization being proposed and also practically realized. Theoretical analysis and experimental results show that the proposed idea and methods are feasible to serve as in the practical application of the RL & SM system.
文摘An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations. In this paper, we show that it is the same as the Nemirovski’s approach, and then modify it to obtain an accelerate Nemirovski’s technique and prove the convergence. Our preliminary computational results are very favorable.
