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Computer Technologies (ICCTech), International Conference on

作者： Fengyuan Shi Di Pan Lihui Jiang Xuan Liu Zhaolun Wen Shanghai Key Laboratory of Aerospace Control Technology Institute Shanghai Aerospace Control Technology Institute Shanghai China

ISBN: (纸本)9781665499194

Aiming at the problem that the ICP (iterative closest point) algorithm tends to fall into the local minimum, a TOF(Time-Of-Flight) camera point cloud registration algorithm based on image fusion is proposed. Taking the advantages of the ability that the TOF camera can obtain depth and grayscale images at the same time, the proposed algorithm performs edge detection on the grayscale and eliminates the edge noise in the corresponding depth image. Then fitting the centroid of the edge to decentralize the query point. Finally, an improved K-Dtree (K-Dimensional tree) algorithm to accelerate the NNS (Nearest Neighbour Search) process between the query points and the model points is proposed, while the 3-Sigma Criteria is adopted to compress the data volume of a single iteration. The simulation results show that compared to traditional ICP algorithm and state-of-the-art methods, the proposed algorithm successfully avoids the point cloud registration process from falling into the local minimum. At the same time, the accurate initial poses are provided and the amount of data is compressed, improving the execution efficiency comprehensively.

关键词： point cloud compression iterative closest point algorithm Image edge detection Heuristic algorithms Fitting Gray-scale Cameras

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Visual SLAM Indoor 3D Map Construction Based on Kinect V1 Camera

Visual SLAM Indoor 3D Map Construction Based on Kinect V1 Ca...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Jun Liu Haolun Sheng Jiabao Zhang Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The visual SLAM (Simultaneous Localization and Mapping) algorithm is widely used in fields such as unmanned driving, robot cruising, and VR, and there is an urgent need to conduct in-depth research on the three-dimensional mapping of the visual SLAM algorithm in various scenarios. For the problem of 3D mapping in indoor environments, the point cloud data collected by the Kinect V1 camera XBOX360 is processed. Based on the ROS system, ORB algorithm is used for extracting feature points, and the iterative closest point algorithm (ICP) is used for map construction, and dense point cloud mapping is performed using existing datasets to achieve a complete 3D map construction of the indoor environment.

关键词： point cloud compression Visualization Three-dimensional displays Simultaneous localization and mapping Accuracy iterative closest point algorithm Signal processing algorithms Feature extraction Cameras Indoor environment

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LOL: Lidar-only Odometry and Localization in 3D point cloud maps

LOL: Lidar-only Odometry and Localization in 3D point cloud ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Rozenberszki, David Majdik, Andras L. Hungarian Acad Sci Inst Comp Sci & Control Machine Percept Res Lab MTA SZTAKI H-1111 Budapest Hungary

ISBN: (纸本)9781728173955

In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidar-only odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle's trajectory were significantly improved in every case, while still being able to maintain real-time performance.

关键词： Three-dimensional displays Laser radar Sensors iterative closest point algorithm Image color analysis Trajectory Real-time systems

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Rigid 3-D Registration: A Simple Method Free of SVD and Eigendecomposition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第10期69卷 8288-8303页

作者： Wu, Jin Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Tencent Robot X Shenzhen 518000 Peoples R China

The 3-D registration is extensively required in many industrial applications involving 3-D perception and reconstruction. However, conventional methods consisting of singular value decomposition (SVD) or eigendecomposition are all hard to be implemented and are difficult to be ported using simple digital circuit prototypes. A novel solution is obtained to solve the rigid 3-D registration problem, motivated by previous eigendecomposition approaches. Different from existing solvers, the proposed algorithm does not require sophisticated matrix operations, e.g., SVD or eigenvalue decomposition. Instead, the optimal eigenvector of the point cross-covariance matrix can be computed within several iterations. It is also proven that the optimal rotation matrix can be directly computed for cases without the need for a quaternion. The simple framework provides a very easy approach, even without floating-number processing. Simulations on noise-corrupted point clouds have verified the robustness and computation speed of the proposed method. The final results indicate that the proposed algorithm is accurate, robust, and owns over 60%-80% less computation time than representatives. It has also been applied to real-world applications for faster robotic visual perception.

关键词： Three-dimensional displays Eigenvalues and eigenfunctions Quaternions iterative closest point algorithm Robustness Robot kinematics 3-D registration computational burden eigenvalue problem point cloud rigid transformation

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RPM-Net: Robust point Matching using Learned Features

RPM-Net: Robust Point Matching using Learned Features

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yew, Zi Jian Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

iterative closest point (ICP) solves the rigid point cloud registration problem iteratively in two steps: (1) make hard assignments of spatially closest point correspondences, and then (2) find the least-squares rigid transformation. The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima. In this paper, we propose the RPM-Net - a less sensitive to initialization and more robust deep learning-based approach for rigid point cloud registration. To this end, our network uses the differentiable Sinkhorn layer and annealing to get soft assignments of point correspondences from hybrid features learned from both spatial coordinates and local geometry. To further improve registration performance, we introduce a secondary network to predict optimal annealing parameters. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility. Experimental results show that our RPM-Net achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code is available at the project website(1).

关键词： Three-dimensional displays iterative closest point algorithm Feature extraction Robustness Annealing Noise measurement Machine learning

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Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020年 8卷 1800511页

作者： Wadehn, Federico Heldt, Thomas Swiss Fed Inst Technol Dept Elect Engn CH-8092 Zurich Switzerland MIT Inst Med Engn & Sci Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Res Lab Elect Cambridge MA 02139 USA

Objective: Novel applications of transcranial Doppler (TCD) ultrasonography, such as the assessment of cerebral vessel narrowing/occlusion or the non-invasive estimation of intracranial pressure (ICP), require high-quality maximal flow velocity waveforms. However, due to the low signal-to-noise ratio of TCD spectrograms, measuring the maximal flow velocity is challenging. In this work, we propose a calibration-free algorithm for estimating maximal flow velocities from TCD spectrograms and present a pertaining beat-by-beat signal quality index. Methods: Our algorithm performs multiple binary segmentations of the TCD spectrogram and then extracts the pertaining envelopes (maximal flow velocity waveforms) via an edge-following step that incorporates physiological constraints. The candidate maximal flow velocity waveform with the highest signal quality index is finally selected. Results: We evaluated the algorithm on 32 TCD recordings from the middle cerebral and internal carotid arteries in 6 healthy and 12 neurocritical care patients. Compared to manual spectrogram tracings, we obtained a relative error of 1.5, when considering the whole waveform, and a relative error of 3.3 for the peak systolic velocity. Conclusion: The feedback loop between the signal quality assessment and the binary segmentation yields a robust algorithm for maximal flow velocity estimation. Clinical Impact: The algorithm has already been used in our ICP estimation pipeline. By making the code and the data publicly available, we hope that the algorithm will be a useful building block for the development of novel TCD applications that require high-quality flow velocity waveforms.

关键词： Spectrogram Doppler effect Blood Estimation Ultrasonic imaging iterative closest point algorithm Image edge detection Transcranial Doppler ultrasound maximal blood flow velocity envelope tracing signal quality assessment intracranial pressure estimation

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Coarse-to-fine 3D road model registration for traffic video augmentation

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IET IMAGE PROCESSING 2020年第12期14卷 2690-2700页

作者： Cui, Zhichao Li, Yaochen Zhang, Chi Liu, Yuehu Ren, Fuji Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Shaanxi Peoples R China Tokushima Univ Sch Adv Technol & Sci Tokushima Tokushima Japan Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China

This study addresses the problem of non-perspective pose estimation from line correspondences in the traffic scenarios. A coarse-to-fine 3D road registration method is proposed for this problem in two stages. Firstly, the iterative closest point algorithm is exploited to estimate the pose coarsely. An objective function is then established to incorporate the feature correspondences for refining the coarse pose. Besides, the framework including road registration is employed for traffic video augmentation. The framework begins with the inputs of traffic videos, road information from Geographic Information Systems and 3D models of traffic elements (e.g. vehicles, pedestrians). Subsequently, 3D road model generation and point-to-line correspondence establishment are achieved in the preprossessing stage. After road and viewpoint registration, the 3D graphic engine is employed to simulate the traffic scene with the road, viewpoints and traffic elements. The augmented videos are generated by fusing the original frames and newly projected traffic elements. The authors demonstrate the superiority of the proposed registration method by the comparison to state-of-the-arts in both quantitative and qualitative experiments. In addition, the frames of the augmented videos validate the proposed method in the application.

关键词： solid modelling geographic information systems image registration pose estimation iterative methods Geographic Information Systems traffic elements point-to-line correspondence establishment viewpoint registration 3D graphic engine traffic scene viewpoints augmented videos coarse-to-fine 3D road model registration traffic video augmentation nonperspective line correspondences traffic scenarios coarse-to-fine 3D road registration method iterative closest point algorithm feature correspondences framework including road registration traffic videos road information

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AI-ENABLED NEXT-GENERATION COMMUNICATION NETWORKS: INTELLIGENT AGENT AND AI ROUTER

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IEEE WIRELESS COMMUNICATIONS 2020年第6期27卷 129-133页

作者： Jiang, Chunxiao Ge, Ning Kuang, Linling Tsinghua Univ Sch Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

Nowadays, network users' demands for low latency services and individual data security grow rapidly. In this article, we conceive a new communication network architecture based on the concept of intelligent agents. The proposed intelligent agent serves as a virtual secretary of the corresponding user to request desired service from Internet companies or neighboring agents, which can reduce Internet surfing latency and prevent privacy leakage. Moreover, to improve the forwarding efficiency of data packages, a new mixed routing structure and the AI router are also introduced, to support various on-demand services with low latency. Overall, the proposed communication network architecture is expected to be more efficient than the existing one, and inspire readers to deliberate the revolution of future communications networks.

关键词： Intelligent agents iterative closest point algorithm Network architecture Artificial intelligence Routing Communication networks Next generation networking

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A Coarse-to-Fine Generalized-ICP algorithm With Trimmed Strategy

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IEEE ACCESS 2020年 8卷 40692-40703页

作者： Wang, Xin Li, Yun Peng, Yaxin Ying, Shihui Shanghai Univ Sch Sci Dept Math Shanghai 200444 Peoples R China

In this paper, we introduce a modified Generalized iterative closest point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods.

关键词： iterative closest point algorithm Robustness Principal component analysis Covariance matrices Noise measurement Licenses Mirrors Registration modified GICP trimmed method

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Non-Uniform Network Mapping and Localization

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IEEE ACCESS 2020年 8卷 79708-79715页

作者： Liu, Wei Yang, Yanxi Wang, Wenqing Xian Univ Technol Coll Automat & Informat Engn Dept Informat & Control Engn Xian 710048 Peoples R China

The purpose of this paper is to establish a new simultaneous localization and mapping (SLAM) framework, which can make better use of the spatial structure of the environment, to improve the robustness and reliability of the system. To locate, a good map should be not only a representation of the real environment, but also a highly compressed and intelligently organized geography information of the environment. Furthermore, the map structure should be stable and robust to inevitable errors. Inspired by human positioning ability, we developed a non-uniform network mapping and localization algorithm (NU-SLAM) based on the complex network theory. Landmarks are classified and sparsely connected according to their localization ability. Therefore, data association and information transmission can be carried out hierarchically with low computational cost and robustness to data association errors. In this paper, a new visual to the mapping and localization problem is proposed. Simulation results show that the algorithm can achieve good results.

关键词： Simultaneous localization and mapping Robustness iterative closest point algorithm Licenses Complexity theory Hierarchical mapping non-uniform network SLAM

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