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Artificial intelligence for robotics : build intelligent robots that perform human tasks using AI...

2018年

作者： Govers Francis X.

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Proceedings of SPIE-IS and T Electronic Imaging - intelligent robots and computer vision xxviii: algorithms and techniques

Proceedings of SPIE-IS and T Electronic Imaging - Intelligen...

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intelligent robots and computer vision xxviii: algorithms and techniques

ISBN: (纸本)9780819484154

The proceedings contain 32 papers. The topics discussed include: software framework for nano- and microscale measurement applications;a traffic situation analysis system;stereo matching based on two cameras and one 3D image sensor;linear stereo vision based objects detection and tracking using spectral clustering;real-time car detection system;real-time people and vehicle detection from UAV imagery;real-time pose invariant logo and pattern detection;study of temporal modified-RANSAC based method for the extraction and 3D shape reconstruction of moving objects from dynamic stereo images and for estimating the camera pose;a multiple feature based particle filter using mutual information maximization;high precision object segmentation and tracking for use in super resolution video reconstruction;robust pedestrian detection and tracking from a moving vehicle;and a target detection method in multimodal images with complex backgrounds and different views.

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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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CAMAL: Context-aware multi-scale attention framework for lightweight visual place recognition

arXiv

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arXiv 2019年

作者： Khaliq, Ahmad Ehsan, Shoaib Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab in School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multilayer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Artificial Intelligence for Robotics : Build intelligent robots That Perform Human Tasks Using AI techniques

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2018年

作者： Govers, Francis X. Packt Publishing, Limited

Bring a new degree of interconnectivity to your world by building your own intelligent robots Key Features Leverage fundamentals of AI and robotics Work through use cases to implement various machine learning algorithms Explore Natural Language Processing (NLP) concepts for efficient decision making in robots Book Description Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence. What you will learn Get started with robotics and artificial intelligence Apply simulation techniques to give your robot an artificial personality Understand object recognition using neural networks and supervised learning techniques Pick up objects using genetic algorithms for manipulation Teach your robot to listen using NLP via an expert system Use machine learning and computer vision to teach your robot how to avoid obstacles Understand path planning, decision trees, and search algorithms in order to enhance your robot Who this book is for If you have basic knowledge about robotics and want to build or enhance your existing robot's intelligence, then Artificial Intelligence for Robotics is for you. This book is also for enthusiasts who want to gain know

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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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Defect Detection in Porcelain Industry based on Deep Learning techniques 19

Defect Detection in Porcelain Industry based on Deep Learnin...

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19th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Birlutiu, Adriana Burlacu, Adrian Kadar, Manuella Onita, Daniela 1 Decembrie 1918 Univ Alba Iulia Gabriel Bethlen 5 Alba Iulia 510009 Romania Gheorghe Asachi Tech Univ Iasi Bd Prof Dimitrie Mangeron 67 Iasi 700050 Romania

ISBN: (纸本)9781538626269

This paper presents an automated defect management system based on machine learning and computer vision that detects and quantifies different types of defects in porcelain products. The system is developed in collaboration with an industrial porcelain producer and integrates robots, artificial vision and machine learning. At present, in most of the companies involved in the porcelain industry, defect detection is performed manually by employees. An intelligent system for product monitoring and defect detection is very much needed. Our proposed system is implemented through a convolutional neural network which analyzes images of the products and predicts if the product is defective or not. Experimental evaluation on an image data set acquired at the industrial partner shows promising results. The proposed architecture will finally have a positive economic impact for the company by optimizing the production flow and reducing the production costs.

关键词： deep learning defects porcelain

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