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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Xu, Song Zhou, Huaidong Chou, Wusheng Beihang University School of Mechanical Engineering and Automation Beijing China Beihang University School of Mechanical Engineering and Automation State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9781728163215

Autonomous navigation is a basic prerequisite for mobile robot to realize environmental exploration. Current navigation methods are mainly based on metric maps, which require precise geometric coordinates and lack the capability to efficiently store semantic information of the environment. In this paper, we present a visual topological mapping and navigation method for mobile robot in large-scale environment, which is similar to the human navigation system. Topological map represents the environment as a topology diagram with nodes and edges in which the topological nodes record local semantic information of the environment, such as visual features, robot pose and scene properties. In the topological navigation stage, an image-based Monte Carlo localization is proposed to estimate the semantic pose of robot which can help robot judge whether it has reached the target location more flexibility. Experiments are conducted in real world environments and results indicate that the proposed system exhibits great performance in robustness of navigation. © 2019 IEEE.

关键词： Mobile robots

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Learning open set network with discriminative reciprocal points

arXiv

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arXiv 2020年

作者： Chen, Guangyao Qiao, Limeng Shi, Yemin Peng, Peixi Li, Jia Huang, Tiejun Pu, Shiliang Tian, Yonghong Department of Computer Science and Technology Peking University China State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University China Peng Cheng Laborotory Shenzhen China Hikvision Research Institute Hangzhou China

Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as’unknown’. In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data. In this paper, we propose a new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category. The sample can be classified to known or unknown by the otherness with reciprocal points. To tackle the open set problem, we offer a novel open space risk regularization term. Based on the bounded space constructed by reciprocal points, the risk of unknown is reduced through multi-category interaction. The novel learning framework called Reciprocal Point Learning (RPL), which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations. Moreover, we further construct a new large-scale challenging aircraft dataset for open set recognition: Aircraft 300 (Air-300). Extensive experiments on multiple benchmark datasets indicate that our framework is significantly superior to other existing approaches and achieves state-of-the-art performance on standard open set benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Aircraft

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Haptic display for virtual reality: progress and challenges

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virtual reality & Intelligent Hardware 2019年第2期1卷 136-162页

作者： Dangxiao WANG Yuan GUO Shiyi LIU Yuru ZHANG Weiliang XU Jing XIAO State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100083China Peng Cheng Laboratory Shenzhen 518055China Department of Mechanical Engineering University of AucklandAuckland 1142New Zealand School of Computer Science and the Robotics Engineering Program Worcester Polytechnic InstituteWorcesterMA 01609-2280USA

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

关键词： Haptic display virtual reality Desktop haptics Surface haptics Wearable haptics Multimodal haptics

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Multibranch Cnn-Based Pansharpening With Skip Connection

Multibranch Cnn-Based Pansharpening With Skip Connection

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Mohammed El Amin Larabi Moussa Sofiane Karoui Souleyman Chaib Khadidja Bakhti Mohammed Ilyas Tchenar Agence Spatial Algérienne Centre des Techniques Spatiale Arzew Algérie School of computer science Harbin Institute of Technology China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (数字)9781728121901

ISBN: (纸本)9781728121918

Recent research on multispectral (MS) and panchromatic (PN) images fusion that known as pansharpening has progressed with the development of Convolutional Neural Networks (CNN). However, the states-of-the-arts methods are principally based on simple networks with shallow architectures that may limit their performance. Recently, residual learning (ResNet) exhibit improved performance in many application domains. At the same time, numerous upsampling methods were developed, from the classical interpolation to learning based methods. In this paper, ResNet is employed to make the full exploitation of the high nonlinearity of CNN. Moreover, an ensemble of upsampling methods were joined in the developed Multibranch Pansharpening Network (MPN) that prove good performance to reconstruct high-resolution MS images. The proposed approach is applied to QuickBird data, its efficiency is assessed with universally used performance criteria in spatial and spectral domains. Experimental results of the proposed approach show better spatial performance than classical methods and competitive spectral performance against the state-of-the-art approaches.

关键词： CNN Deep Learning super-resolution pansharpening Transposed Convolution. super-resolution upsampling Performance metrics image fusion Spectral domain Connections

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Real-Time UAV Path Planning for Autonomous Urban Scene Reconstruction

Real-Time UAV Path Planning for Autonomous Urban Scene Recon...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qi Kuang Jinbo Wu Jia Pan Bin Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science the University of Hong Kong Hong Kong China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Unmanned aerial vehicles (UAVs) are frequently used for large-scale scene mapping and reconstruction. However, in most cases, drones are operated manually, which should be more effective and intelligent. In this article, we present a method of real-time UAV path planning for autonomous urban scene reconstruction. Considering the obstacles and time costs, we utilize the top view to generate the initial path. Then we estimate the building heights and take close-up pictures that reveal building details through a SLAM framework. To predict the coverage of the scene, we propose a novel method which combines information on reconstructed point clouds and possible coverage areas. The experimental results reveal that the reconstruction quality of our method is good enough. Our method is also more time-saving than the state-of-the-arts.

关键词： Buildings Image reconstruction Three-dimensional displays Path planning Drones Cameras Layout

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Adaptive sliding mode back-stepping control for hypersonic flight vehicle with state constraints and unmodeled disturbances

Adaptive sliding mode back-stepping control for hypersonic f...

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International Conference on Control and Automation (ICCA)

作者： Fei Ma Yunjie Wu Xiaocen Liu Bohao Li Xiaodong Liu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China and Technology on Aircraft Control Laboratory Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This study presents a finite-time adaptive sliding mode back-stepping controller (FASMBC) for hypersonic flight vehicle (HFV), where the state constraints and unmodeled disturbances are considered. Firstly, the longitudinal model of HFV is decomposed into altitude subsystem and velocity subsystem. Then, sliding mode control and back-stepping logic based on barrier Lyapunov function are merged to design the robust controller for each subsystem. Besides, the adaptive strategy is applied to estimate the unmodeled disturbances. A tracking differentiator is introduced to avoid the "explosion of terms" problem involved in traditional back-stepping control. Finally, the simulation results is provided to illustrate the effectiveness of the proposed control scheme.

关键词： Solid modeling Adaptation models Lyapunov methods Estimation Atmospheric modeling Two dimensional displays Three-dimensional displays

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ESNet: An efficient symmetric network for real-time semantic segmentation 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Wang, Yu Zhou, Quan Xiong, Jian Wu, Xiaofu Jin, Xin National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Beijing Electronic Science and Technology Institute Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

The recent years have witnessed great advances for semantic segmentation using deep convolutional neural networks (DCNNs). However, a large number of convolutional layers and feature channels lead to semantic segmentation as a computationally heavy task, which is disadvantage to the scenario with limited resources. In this paper, we design an efficient symmetric network, called (ESNet ), to address this problem. The whole network has nearly symmetric architecture, which is mainly composed of a series of factorized convolution unit (FCU) and its parallel counterparts. On one hand, the FCU adopts a widely-used 1D factorized convolution in residual layers. On the other hand, the parallel version employs a transform-split-transform-merge strategy in the designment of residual module, where the split branch adopts dilated convolutions with different rate to enlarge receptive field. Our model has nearly 1.6M parameters, and is able to be performed over 62 FPS on a single GTX 1080Ti GPU. The experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off for real-time semantic segmentation on CityScapes dataset. © Springer Nature Switzerland AG 2019.

关键词： Convolution

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Laplacian-based 3D mesh simplification with feature preservation

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International Journal of Modeling, Simulation, and Scientific Computing 2019年第2期10卷 64-82页

作者： Wei Lyu Wei Wu Lin Zhang Zhaohui Wu Zhong Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 P.R.China Generic Technology Center Systems Engineering Research Institute Beijing 100036 P.R.China Transportation Basic Research and Applied Innovation Center China Academy of Transportation Sciences Beijing 100029 P.R.China

We propose a novel Laplacian-based algorithm that simplifies triangle surface meshes and can provide different preservation ratios of geometric *** efficient and fast algorithm uses a 3D mesh model as input and initially detects geometric features by using a Laplacian-based shape descriptor(L-descriptor).The algorithm further performs an optimized clustering approach that combines a Laplacian operator with K-means clustering algorithm to perform vertex ***,we introduce a Laplacian weighted cost function based on L-descriptor to perform feature weighting and error statistics comparison,which are further used to change the deletion order of the model elements and preserve the saliency *** algorithm can provide different preservation ratios of geometric features and may be extended to handle arbitrary mesh *** experiments on a variety of 3D surface meshes demonstrate the advantages of our algorithm in terms of improving accuracy and applicability,and preserving saliency geometric features.

关键词： Mesh simplification L-descriptor feature weighting feature metric feature preservation

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AutoETER: automated entity type representation for knowledge graph embedding

arXiv

引用

arXiv 2020年

作者： Niu, Guanglin Li, Bo Zhang, Yongfei Pu, Shiliang Li, Jingyang Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Pengcheng Laboratory Shenzhen518055 China Hikvision Research Institute Hangzhou311500 China

Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type information can improve the representation of entities which however totally rely on the explicit types or neglect the diverse type representations specific to various relations. Besides, none of the existing methods is capable of inferring all the relation patterns of symmetry, inversion and composition as well as the complex properties of 1-N, N-1 and N-N relations, simultaneously. To explore the type information for any KG, we develop a novel KGE framework with Automated Entity TypE Representation (AutoETER), which learns the latent type embedding of each entity by regarding each relation as a translation operation between the types of two entities with a relation-aware projection mechanism. Particularly, our designed automated type representation learning mechanism is a pluggable module which can be easily incorporated with any KGE model. Besides, our approach could model and infer all the relation patterns and complex relations. Experiments on four datasets demonstrate the superior performance of our model compared to state-of-the-art baselines on link prediction tasks, and the visualization of type clustering provides clearly the explanation of type embeddings and verifies the effectiveness of our model. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Semi-supervised hyperspectral image classification with graph clustering convolutional networks

arXiv

引用

arXiv 2020年

作者： Zeng, Hao Liu, Qingjie Zhang, Mingming Han, Xiaoqing Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Xueyuan Road Haidian District Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Beijing Research Institute of Uranium Geology Beijing100029 China

Hyperspectral image classification (HIC) is an important but challenging task, and a problem that limits the algorithmic development in this field is that the ground truths of hyperspectral image (HSIs) are extremely hard to obtain. Recently a handful of HIC methods are developed based on the graph convolution networks (GCNs), which effectively relieves the scarcity of labeled data for deep learning based HIC methods. To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size. In particular, we first cluster the pixels with similar spectral features into a superpixel, and build the graph based on the superpixels of the input HSI. Then instead of performing convolution over this suerperpixel graph, we further partition it into several sub-graphs by pruning the edges with weak weights, so as to strengthen the correlations of nodes with high similarity. This second round of clustering also further reduces the graph size, thus reducing computation burden of graph convolution. Experimental results on three widely used benchmark datasets well prove the effectiveness of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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