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arXiv 2023年

作者： Xia, Zimin Booij, Olaf Kooij, Julian F.P. The Intelligent Vehicles Group Department of Cognitive Robotics Delft University of Technology Leeghwaterstraat Delft2628 CN Netherlands The Computer Vision Lab Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query’s local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The orientation-aware descriptors are obtained by using a translationally equivariant convolutional ground image encoder and contrastive learning. The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module. A smaller Orientation Decoder produces a vector field to condition the orientation estimate on the localization. Our method is validated on the VIGOR and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and 36% in median localization error for comparable orientation estimation accuracy. The predicted probability distribution can represent localization ambiguity, and enables rejecting possible erroneous predictions. Without re-training, the model can infer on ground images with different field of views and utilize orientation priors if available. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle’s pose over time, achieving a median localization error under 1 meter and a median orientation error of around 1 degree at 14 FPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer Science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Task-Oriented Grasp Prediction with Visual-Language Inputs

Task-Oriented Grasp Prediction with Visual-Language Inputs

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chao Tang Dehao Huang Lingxiao Meng Weiyu Liu Hong Zhang Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Stanford University United States

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://***/watch?v=e1wfYQPeAXU.

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arXiv

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arXiv 2024年

作者： Lin, Zhenchao He, Li Yang, Hongqiang Sun, Xiaoqun Zhang, Guojin Chen, Weinan Guan, Yisheng Zhang, Hong School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China Meituan Technology Co. Ltd Shenzhen China

Large-scale point cloud consists of a multitude of individual objects, thereby encompassing rich structural and underlying semantic contextual information, resulting in a challenging problem in efficiently segmenting a point cloud. Most existing researches mainly focus on capturing intricate local features without giving due consideration to global ones, thus failing to leverage semantic context. In this paper, we propose a Similarity-Weighted Convolution and local-global Fusion Network, named SWCF-Net, which takes into account both local and global features. We propose a Similarity-Weighted Convolution (SWConv) to effectively extract local features, where similarity weights are incorporated into the convolution operation to enhance the generalization capabilities. Then, we employ a downsampling operation on the K and V channels within the attention module, thereby reducing the quadratic complexity to linear, enabling Transformer to deal with large-scale point cloud. At last, orthogonal components are extracted in the global features and then aggregated with local features, thereby eliminating redundant information between local and global features and consequently promoting efficiency. We evaluate SWCF-Net on large-scale outdoor datasets SemanticKITTI and Toronto3D. Our experimental results demonstrate the effectiveness of the proposed network. Our method achieves a competitive result with less computational cost, and is able to handle large-scale point clouds efficiently. The code is available at https://***/Sylva-Lin/SWCF-Net Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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2mm Diameter Continuum Robot Tools for Suturing in Open Spina Bifida Repair

2mm Diameter Continuum Robot Tools for Suturing in Open Spin...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Arion Law Nillan Nimal Paul H. Kang Radian Gondokaryono James Drake Tim Van Mieghem Thomas Looi Department of Mechanical and Industrial Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Institute of Biomedical Engineering University of Toronto Canada Department of Computer Science and the Medical Computer Vision and Robotics Lab University of Toronto Canada Mount Sinai Hospital Toronto Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Open Spina Bifida (OSB) is a congenital neural tube defect where a major component of the procedure to repair the defect involves the closure of a lesion wound through suturing. For a minimally invasive approach, tools entering the uterus to access the fetus should be as thin as possible to minimize maternal risk. This work presents the design of a 3 degrees-of-freedom, 2mm diameter tool wrist with a bending range of motion from 0° to 90°. This wrist is capable of generating up to 2N of force measured from the end of the wrist and achieving a bending curvature of 107m -1 (9.35mm bending radius). A pseudo-rigid body kinematic model has been implemented for the control of this tool with a protocol for singularity mitigation and avoidance. Timed teleoperation studies explicitly demonstrate that the tool is able to reliably execute suturing with a fastest achieved time of under 3 minutes for a simple interrupted suturing technique.

关键词： Wrist Protocols Prevention and mitigation Uterus Force Bending Maintenance engineering Wounds Reliability engineering Motion control

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Adaptive Generalized Social Space Delimitation for Human-Robot Interaction Tasks

Adaptive Generalized Social Space Delimitation for Human-Rob...

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IEEE Latin American robotics Symposium, LARS

作者： Manuela M. F. Silva Aline F. F. Silva Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Laboratório de Inteligência Computacional e Robótica (LICRo) Instituto Federal do Triângulo Mineiro (IFTM) Campus Patrocínio Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

Human-Robot Interaction (HRI) research is of paramount importance as it addresses the rapidly evolving landscape where robots are increasingly integrated into everyday life. Social spaces refer to the invisible zones that individuals maintain around themselves, dictating appropriate distances for various types of interactions. This concept is pivotal in HRI, as it ensures that robots engage with humans in ways that are both comfortable and respectful of personal boundaries. In this paper, we propose a novel social space delimitation approach based on deformable elastic bands. Our method leverages the flexibility and adaptability of elastic band theory to dynamically define and adjust social spaces around individuals and groups. The results, based on both static and dynamic cases as well as a real-world dataset, show that our approach achieves a responsive and accurate representation of social spaces, that can accommodate varying group configurations and environmental conditions.

关键词： Accuracy Human-robot interaction Robots

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Unsupervised and explainable assessment of video similarity 30

Unsupervised and explainable assessment of video similarity

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30th British Machine vision Conference, BMVC 2019

作者： Papoutsakis, Konstantinos Argyros, Antonis Computer Science Department University of Crete Greece Computational Vision and Robotics Lab Institute of Computer Science FORTH Greece

We propose a novel unsupervised method that assesses the similarity of two videos on the basis of the estimated relatedness of the objects and their behavior, and provides arguments supporting this assessment. A video is represented as a complete undirected action graph that encapsulates information on the types of objects and the way they (inter)act. The similarity of a pair of videos is estimated based on the bipartite Graph Edit Distance (GED) of the corresponding action graphs. As a consequence, on-top of estimating a quantitative measure of video similarity, our method establishes spatiotemporal correspondences between objects across videos if these objects are semantically related, if/when they interact similarly, or both. We consider this an important step towards explainable assessment of video and action similarity. The proposed method is evaluated on a publicly available dataset on the tasks of activity classification and ranking and is shown to compare favorably to state of the art supervised learning methods. © 2019. The copyright of this document resides with its authors.

关键词： computer vision

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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration

Benchmarking Classical and Learning-Based Multibeam Point Cl...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Li Ling Jun Zhang Nils Bore John Folkesson Anna Wåhlin Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Stockholm Sweden Institute of Computer Graphics and Vision (ICGV) TU Graz Austria Ocean Infinity Västra Frölunda Sweden Department of Marine Sciences University of Gothenburg Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES dataset. To facilitate future research, both the code and data are made available online. 1

关键词： Point cloud compression Learning systems Measurement Three-dimensional displays Simultaneous localization and mapping Iterative closest point algorithm Transforms

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A vision Based Hardware-Software Real-Time Control System for the Autonomous Landing of an UAV 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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