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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

Emergency Clinical Procedure Detection With Deep Learning

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Emergency Clinical Procedure Detection With Deep Learning

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Lingfeng Li Richard A. Paris Conner Pinson Yan Wang Joseph Coco Jamison Heard Julie A. Adams Daniel V. Fabbri Bobby Bodenheimer Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee Vanderbilt University Medical Center Nashville Tennessee Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY Collaborative Robotics and Intelligent Systems Institute Oregon State University Oregon

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.

关键词： Adaptation models Training Testing Machine learning Hospitals Cameras

Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

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arXiv 2022年

作者： Li, Wenli He, Pei Fan, Yulong Du, Yangtao Gao, Bo Chu, Zhiqin An, Chengxu Lei, Dangyuan Yuan, Weizheng Yu, Yiting Research & Development Institute of Northwestern Polytechnical University in Shenzhen College of Mechanical Engineering Ningbo Institute of Northwestern Polytechnical University Northwestern Polytechnical University Xi'An710072 China Northwestern Polytechnical University Xi'An710072 China Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'An710072 China Department of Materials Science and Engineering City University of Hong Kong 999077 Hong Kong The Institute of Ai and Robotics Fudan University Shanghai200433 China Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xian710119 China Department of Electrical and Electronic Engineering Joint Appointment with School of Biomedical Sciences The University of Hong Kong 999077 Hong Kong

Multicolor super-resolution imaging remains an intractable challenge for both far-field and near-field based super-resolution techniques. Planar super-oscillatory lens (SOL), a far-field subwavelength-focusing diffractive lens device, holds great potential for achieving sub-diffraction-limit imaging at multiple wavelengths. However, conventional SOL devices suffer from a numerical aperture (NA) related intrinsic tradeoff among the depth of focus (DoF), chromatic dispersion and focus spot size, being an essential characteristics of common diffractive optical elements. Typically, the limited DoF and significant chromatism associated with high NA can lead to unfavorable degradation of image quality although increasing NA imporves the resolution. Here, we apply a multi-objective genetic algorithm (GA) optimization approach to design an apochromatic binary-phase SOL that generates axially jointed multifoci concurrently having prolonged DoF, customized working distance (WD) and suppressed side-lobes yet minimized main-lobe size, optimizing the aforementioned NA-dependent tradeoff. Experimental implementation of this GA-optimized SOL demonstrates simultaneous focusing of blue, green and red light beams into an optical needle of ∼0.5λ in diameter and >10λ in length (DoF) at 428 μm WD, resulting in an ultimate resolution better than λ/3 in the lateral dimension. By integrating this apochromatic SOL device with a commercial fluorescence microscope, we employ the optical needle to perform, for the first time, three-dimensional super-resolution multicolor fluorescence imaging of the "unseen" fine structure of neurons at one go. The present study provides not only a practical route to far-field multicolor super-resolution imaging but also a viable approach for constructing imaging systems avoiding complex sample positioning and unfavorable photobleaching. Copyright © 2022, The Authors. All rights reserved.

关键词： Needles

Efficient State Abstraction using Object-centered Predicates for Manipulation Planning

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arXiv 2020年

作者： Agostini, Alejandro Lee, Dongheui Human-centered Assistive Robotics Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Intelligent and Interactive Systems Department of Computer Science University of Innsbruck Innsbruck6020 Austria Institute of Robotics and Mechatronics Wessling Germany

The definition of symbolic descriptions that consistently represent relevant geometrical aspects in manipulation tasks is a challenging problem that has received little attention in the robotic community. This definition is usually done from an observer perspective of a finite set of object relations and orientations that only satisfy geometrical constraints to execute experiments in laboratory conditions. This restricts the possible changes with manipulation actions in the object configuration space to those compatible with that particular external reference definitions, which greatly limits the spectrum of possible manipulations. To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency in manipulation tasks. This object-centered description is directly obtained from the poses and bounding boxes of objects using a novel learning mechanisms that permits generating signal-symbols relations without the need of handcrafting these relations for each particular scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Wave functions

Human-collective visualization transparency

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arXiv 2020年

作者： Roundtree, Karina A. Cody, Jason R. Leaf, Jennifer Onan Demirel, H. Adams, Julie A. School of Mechanical Industrial and Manufacturing Engineering Oregon State University Department of Electrical Engineering and Computer Science United States Military Academy Collaborative Robotics and Intelligent Systems Institute Oregon State University

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence human-collective behavior. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collectives current state and overall behaviors, which can cause poor teaming performance. An analysis of visualization transparency and the derived visualization design guidance, based on remote supervision of collectives, are the primary contributions of this manuscript. The individual agent and abstract visualizations were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

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Using Machine Learning Approaches to Localization in an Embe...

IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Luan C. Klein João Braun Felipe N. Martins Heinrich Wörtche Andre Schneider de Oliveira João Mendes Vítor H. Pinto Paulo Costa José Lima Universidade Tecnológica Federal do Paraná Campus Curitiba UTFPR/PR Brasil Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Braganca Campus de Santa Apolonia Portugal Laboratory for Sustainability and Technology in Mountain Regions (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Faculty of Engineering of University of Porto Portugal INESC Technology and Science Porto Portugal Sensors and Smart Systems Group Institute of Engineering Hanze University of Applied Sciences The Netherlands Department of Electrical Engineering Eindhoven University of Technology The Netherlands SYSTEC (DIGI2) ARISE & ECE Dept. Fac. de Engenharia Universidade do Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

关键词： Location awareness Training Embedded systems Machine learning algorithms Navigation Decision making Machine learning

Learning stability certificates from data

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arXiv 2020年

作者： Boffi, Nicholas M. Tu, Stephen Matni, Nikolai Slotine, Jean-Jacques E. Sindhwani, Vikas Google Brain Robotics John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Electrical and Systems Engineering University of Pennsylvania United States Nonlinear Systems Laboratory Massachusetts Institute of Technology United States

Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property. However, algorithms for synthesizing certificate functions typically require a closed-form analytical expression of the underlying dynamics, which rules out their use on many modern robotic platforms. To circumvent this issue, we develop algorithms for learning certificate functions only from trajectory data. We establish bounds on the generalization error – the probability that a certificate will not certify a new, unseen trajectory – when learning from trajectories, and we convert such generalization error bounds into global stability guarantees. We demonstrate empirically that certificates for complex dynamics can be efficiently learned, and that the learned certificates can be used for downstream tasks such as adaptive control. Copyright © 2020, The Authors. All rights reserved.

关键词： Trajectories

Markerless Multi-View-based Multi-User Head Tracking System for Virtual Reality Applications

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Markerless Multi-View-based Multi-User Head Tracking System ...

IEEE International Conference on systems, Man and Cybernetics

作者： Dylan Bicho Pedro Girão João Paulo Luís Garrote Urbano J. Nunes Paulo Peixoto Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra

In this paper, we propose a markerless multi-view-based multi-user head tracking system for VR/AR/MR applications. In the Virtual Reality domain, the user typically interacts with the virtual world through his movements. The point-of-view of the user's head mounted display (HMD) is based on the user's head pose. Most systems track the head of the user based on the tracking of the HMD itself and are typically constrained to a single user. Aiming to improve the user's experience in complex dynamic environments, we propose a head tracking method based on a multi-camera scenario, able to deal with the interaction of multiple users while coping with the existence of dynamic objects. The method relies on the voxelization of dense point cloud data, which is segmented to detect elements in the scene. Each element is holistically tracked along time and each detected user has his head's position tracked using a dedicated Particle Filter. The system was evaluated using an annotated ground-truth, according to several metrics, and demonstrated a mean 3D positioning error below 18 mm. Furthermore, tests with multiple users were performed that successfully validate the proposed head tracking approach. The aforementioned tests also showed that the proposed approach is computationally efficient (running time below 20 ms for 4 users).

关键词： Three-dimensional displays Head Resists Sensors Cameras Virtual reality Tracking

IEEGview: An open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes

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IEEGview: An open-source multifunction GUI-based Matlab tool...

作者： Li, Guangye Jiang, Shize Chen, Chen Brunner, Peter Wu, Zehan Schalk, Gerwin Chen, Liang Zhang, Dingguo State Key Laboratory of Mechanical Systems and Vibrations Institute of Robotics Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Department of Neurology Albany Medical College AlbanyNY United States National Center for Adaptive Neurotechnologies Wadsworth Center New York State Department of Health AlbanyNY United States Department of Electronic and Electrical Engineering University of Bath Bath United Kingdom

Objective. The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes. Approach. iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment. Main results. All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes. Significance. iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies. © 2019 IOP Publishing Ltd.

关键词： Electrodes