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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXXI - Algorithms and Techniques"

共 372 条记录，以下是21-30 订阅

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Towards vision-Based Impedance Control for the Contact Inspection of Unknown Generically-Shaped Surfaces with a Fully-Actuated UAV

Towards Vision-Based Impedance Control for the Contact Inspe...

引用

2020 IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Ramy Rashad Davide Bicego Ran Jiao Santiago Sanchez-Escalonilla Stefano Stramigioli Robotics and Mechatronics group University of Twente Enschede The Netherlands School of Mechanical Engineering and Automation Beihang University Beijing China Robotics and Mechatronics group University of Twente and ITMO University Saint Petersburg Russia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

The integration of computer vision techniques for the accomplishment of autonomous interaction tasks represents a challenging research direction in the context of aerial robotics. In this paper, we consider the problem of contact-based inspection of a textured target of unknown geometry and pose. Exploiting state of the art techniques in computer graphics, tuned and improved for the task at hand, we designed a framework for the projection of a desired trajectory for the robot end-effector on a generically-shaped surface to be inspected. Combining these results with previous work on energy-based interaction control, we are laying the basis of what we call vision-based impedance control paradigm. To demonstrate the feasibility and the effectiveness of our methodology, we present the results of both realistic ROS/Gazebo simulations and preliminary experiments with a fully-actuated hexarotor interacting with heterogeneous curved surfaces whose geometric description is not available a priori, provided that enough visual features on the target are naturally or artificially available to allow the integration of localization and mapping algorithms.

关键词： Surface impedance Inspection End effectors Trajectory Surface texture Impedance Task analysis

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3D vision for Precision Dairy Farming 6th

3D Vision for Precision Dairy Farming

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6th International-Federation-of-Automatic-Control (IFAC) conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： O'Mahony, Niall Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Riordan, Daniel Walsh, Joseph Inst Technol Tralee IMaR Res Ctr Tralee Co Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automation and Robotics in Agriculture Sensing and Automation in Animal Fanning

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Indoor Scene Recognition based on Weighted Voting Schemes

Indoor Scene Recognition based on Weighted Voting Schemes

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European conference on Mobile robots (ECMR)

作者： Hernandez, Alejandra C. Gomez, Clara Derner, Erik Barber, Ramon Carlos III Univ Madrid Dept Syst Engn & Automat Robot Lab Madrid Spain Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Prague Czech Republic

ISBN: (纸本)9781728136059

Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. "office", "classroom" or "kitchen") can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.

关键词： Scene recognition multi-classification scene understanding mobile robots genetic algorithms

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Towards Event-driven Object Detection with Off-the-shelf Deep Learning

Towards Event-driven Object Detection with Off-the-shelf Dee...

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25th IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Iacono, Massimiliano Weber, Stefan Glover, Arren Bartolozzi, Chiara Ist Italiano Tecnol ICub Facil Genoa Italy

ISBN: (纸本)9781538680940

Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, current trends in state-of-the-art visual algorithms rely on deep-learning with networks designed to process colour and intensity information contained in dense arrays, but are notoriously computationally heavy. While the combination of these visual technologies could lead to fast, efficient, and accurate detection and recognition algorithms, it is uncertain whether the compressed event-camera data actually contain the required information for these techniques to discriminate between objects and a cluttered background. This paper presents a pilot study in which off-the-shelf deep-learning is applied to visual events for object detection on the iCub robotic platform, and analyses the impact of temporal integration of the event data. We also present a novel pipeline that bootstraps event-based dataset annotation from mature frame-based algorithms, in order to more quickly generate the required datasets.

关键词： Cameras Robot vision systems Visualization Training Object detection

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Efficient Absolute Orientation Revisited

Efficient Absolute Orientation Revisited

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25th IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Lourakis, Manolis Terzakis, George Fdn Res & Technol Hellas Inst Comp Sci Iraklion Greece Univ Portsmouth Fac Technol Portsmouth Hants England

ISBN: (纸本)9781538680940

Absolute orientation estimation is the determination of the similarity transformation between two sets of corresponding 3D points, a task arising frequently in computer vision and robotics. We have recently proposed an absolute orientation algorithm based on the Fast Optimal Attitude Matrix (FOAM) algorithm from astronautics and demonstrated that it is more efficient computationally compared to widely-used approaches involving costly eigen- and singular-value matrix decompositions. In this work, we compare our FOAM-based solution with several more algorithms derived from attitude estimation techniques and show that further computational savings are possible by employing an algorithm grounded on the Optimal Linear Attitude Estimator (OLAE) method.

关键词： Quaternions Estimation Symmetric matrices Eigenvalues and eigenfunctions Matrix decomposition Covariance matrices computer vision

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Scenario-based simulation of intelligent driving functions using neural networks 9

Scenario-based simulation of intelligent driving functions u...

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9th International conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Kaur, Parampreet Sobti, Rajeev Lovely Profess Univ Sch Comp Sci Engn Jalandhar India

ISBN: (纸本)9781538644300

The modern technological era is witnessing a significant advancement in AI related fields such as machine learning, mobile robots and autonomous vehicles. The success of such systems is immensely dependent on computer vision algorithms. The entirely software enabled intelligent vehicles will soon be hitting the roads in the coming decade. Such self-controlled mobile robots may still be vulnerable to accidents or crashes. Therefore, the industry requires some state of the art techniques to substantiate the safety and protection of its passengers as well as other road users. Simulation based testing methods have been in use from a long time, but the newer smart vehicle innovations require better versions of simulation techniques. We thus provide an improved mechanism for simulation testing to validate safe navigation of cars in variety of traffic scenarios. Neural network approach integrated with agent based modelling is described in this paper.

关键词： Agent based modelling intelligent driving SAE autonomy levels simulation ADAS

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Teaching robots to Predict Human Motion

Teaching Robots to Predict Human Motion

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25th IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Gui, Liang-Yan Zhang, Kevin Wang, Yu-Xiong Liang, Xiaodan Moura, Jose M. F. Veloso, Manuela Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781538680940

Teaching a robot to predict and mimic how a human moves or acts in the near future by observing a series of historical human movements is a crucial first step in human-robot interaction and collaboration. In this paper, we instrument a robot with such a prediction ability by leveraging recent deep learning and computer vision techniques. First, our system takes images from the robot camera as input to produce the corresponding human skeleton based on real-time human pose estimation obtained with the OpenPose library. Then, conditioning on this historical sequence, the robot forecasts plausible motion through a motion predictor, generating a corresponding demonstration. Because of a lack of high-level fidelity validation, existing forecasting algorithms suffer from error accumulation and inaccurate prediction. Inspired by generative adversarial networks (GANs), we introduce a global discriminator that examines whether the predicted sequence is smooth and realistic. Our resulting motion GAN model achieves superior prediction performance to state-of-the-art approaches when evaluated on the standard H3.6M dataset. Based on this motion GAN model, the robot demonstrates its ability to replay the predicted motion in a human-like manner when interacting with a person.

关键词： robots Gallium nitride Generative adversarial networks Predictive models Cameras Skeleton Decoding

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Scenario-based simulation of intelligent driving functions using neural networks

Scenario-based simulation of intelligent driving functions u...

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International conference on Computing, Communication and Networking Technologies

作者： Parampreet Kaur Rajeev Sobti School of Computer Science Engineering Lovely Professional University Jalandhar India

关键词： Agent based modelling intelligent driving SAE autonomy levels Simulation ADAS

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Autonomous flight and vision-based target tracking for a flapping-wing MAV

Autonomous flight and vision-based target tracking for a fla...

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2016 IEEE/RSJ International conference on intelligent robots and Systems, IROS 2016

作者： Ryu, Seungwan Kwon, Ukjin Jin Kim, H. Department of Mechanical and Aerospace Engineering Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781509037629

In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also present autonomous vision-based target tracking for a FWMAV equipped with a low-cost and light-weight first person view (FPV) camera. We construct a ground station, integrated with control and vision algorithms, which performs the image processing and the computation of control inputs based on the acquired state variables from motion capture system. In addition, we employ a vision algorithm for a low-quality camera to detect a static target with the discussions on the techniques to improve the reliability of visual detection. Experimental results show satisfactory flight performance, achieving the height regulation and directional control, and autonomous vision-based target tracking. © 2016 IEEE.

关键词： Cameras

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3D robot perception with Point Cloud Library

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ROBOTICS AND AUTONOMOUS SYSTEMS 2016年 78卷 97-99页

作者： Munaro, Matteo Rusu, Radu B. Menegatti, Emanuele Univ Padua Sch Engn Dept Informat Engn DEI Intelligent Autonomous Syst Lab Via Gradenigo 6-a I-35131 Padua Italy Open Percept Inc 68 Willow Rd Menlo Pk CA 94025 USA

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