For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing...For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.展开更多
In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense s...In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.展开更多
基金supported by National Basic Research Program of China(973 Program)(No.2012CB720000)National Natural Science Foundation of China(Nos.61225015 and 60974011)+3 种基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61321002)Beijing Municipal Natural Science Foundation(Nos.4102053 and 4101001)Beijing Natural Science Foundation(Nos.4132042)Beijing Higher Education Young Elite Teacher Project(No.YETP1212)
文摘For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.
基金supported by National Natural Science Foundation of China(Nos.NSFC 61473042 and 61105092)Beijing Higher Education Young Elite Teacher Project(No.YETP1215)
文摘In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.
