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检索条件"任意字段=Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques"

共 688 条记录，以下是81-90 订阅

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Real time self-localization of omni-vision robot by pattern match system

Real time self-localization of omni-vision robot by pattern ...

引用

International Conference on Advanced Robotics and intelligent Systems (ARIS)

作者： Shu-Yin Chiang Xingzhi Guo Hsien-Wen Hu Department of Information and Telecommunications Engineering Ming Chuan University Taoyuan Taiwan

In this study, we designed an autonomous mobile robot according to the rules of the Federation of International Robot-soccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The real time self-localization scheme of the mobile robot is based on the algorithms extracting white lines from the soccer field featured in the images from the omni-vision system. The proposed algorithm uses the white line distances from 360 degree direction between the robot and the rotation angle of the robot obtained from the gyroscope as a featured vector. The feature vector is compared with the pre-built in patterns of the database. The location of the robot is obtained from the location of the matched pattern with the minimum error of the featured vector. The results demonstrate that the proposed algorithm is accurate, exhibiting a 10 cm (distance) and 1 degree (rotation) position error in a soccer field measuring 750 cm × 550 cm within processing time about 5 milliseconds. The real time self-localization system can be practically implemented in 2014 FIRA Robot Soccer Competition to fulfill the contest requirement.

关键词： Pattern matching Real-time systems Vectors Mobile robots Heuristic algorithms Feature extraction

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Simultaneous On-line Discovery and Improvement of Robotic Skill Options

Simultaneous On-line Discovery and Improvement of Robotic Sk...

引用

IEEE/RSJ International Conference on intelligent robots and Systems

作者： Freek Stulp Laura Herlant Antoine Hoarau Gennaro Raiola Robotics and Computer Vision Ecole Nationale Superieure de Techniques Avancees (ENSTA-ParisTech) France

ISBN: (纸本)9781479969357

The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm "Simultaneous On-line Discovery and Improvement of Robotic Skills" (SODIRS) that is able to autonomously discover and optimize skill options for such task variations. We formalize the problem in a reinforcement learning context, and use the PI~(BB) algorithm [2] to continually optimize skills with respect to a cost function. SODIRS discovers new subskills, or "skill options", by clustering the costs of trials, and determining whether perceptual features are able to predict which cluster a trial will belong to. This enables SODIRS to build a decision tree, in which the leaves contain skill options for task variations. We demonstrate SODIRS' performance in simulation, as well as on a Meka humanoid robot performing the ball-in-cup task.

关键词： Cost function Covariance matrices robots Visualization Vectors Clustering algorithms

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Improving Shape Context Using Geodesic Information And Reflection Invariance

Improving Shape Context Using Geodesic Information And Refle...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Premachandran, Vittal Kakarala, Ramakrishna Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)9780819494351

In this paper, we identify some of the existing problems in shape context matching. We first identify the need for reflection invariance in shape context matching algorithms and propose a method to achieve the same. With the use of these reflection invariance techniques, we bring all the objects, in a database, to their canonical form, which halves the time required to match two shapes using their contexts. We then show how we can build better shape descriptors by the use of geodesic information from the shapes and hence improve upon the well-known Inner Distance Shape Context (IDSC). The IDSC is used by many pre- and post-processing algorithms as the baseline shape-matching algorithm. Our improvements to IDSC will remain compatible for use with those algorithms. Finally, we introduce new comparison metrics that can be used for the comparison of two or more algorithms. We have tested our proposals on the MPEG-7 database and show that our methods significantly outperform the IDSC.

关键词： object recognition shape matching shape descriptor computer vision

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The 20TH Annual intelligent Ground Vehicle Competition: Building a Generation of Robotists

The 20TH Annual Intelligent Ground Vehicle Competition: Buil...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Theisen, Bernard L. Kosinski, Andrew US Army TARDEC Warren MI 48397 USA

ISBN: (纸本)9780819494351

The intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 20 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

关键词： intelligent robots autonomous systems ground vehicles vehicle control engineering education IGVC

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Loop Closure Detection Using Local Zernike Moment Patterns

Loop Closure Detection Using Local Zernike Moment Patterns

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Sariyanidi, Evangelos Sencan, Onur Temeltas, Hakan Istanbul Tech Univ Control & Automat Engn Dept TR-34469 Istanbul Turkey

ISBN: (纸本)9780819494351

This paper introduces a novel image description technique that aims at appearance based loop closure detection for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure is achieved by simply comparing the most recent image with the images in the past trajectory. The technique has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms of computation efficiency and loop closure precision.

关键词： Loop closure mobile robotics image processing SLAM computer vision

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Development of dog-like retrieving capability in a ground robot

Development of dog-like retrieving capability in a ground ro...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： MacKenzie, Douglas C. Ashok, Rahul Rehg, James M. Witus, Gary Mobile Intelligence Corp 13620 Merriman Rd Livonia MI 48150 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Turing Assoc Ann Arbor MI 48103 USA

ISBN: (纸本)9780819494351

This paper presents the Mobile Intelligence Team's approach to addressing the CANINE outdoor ground robot competition. The competition required developing a robot that provided retrieving capabilities similar to a dog, while operating fully autonomously in unstructured environments. The vision team consisted of Mobile Intelligence, the Georgia Institute of Technology, and Wayne State University. Important computer vision aspects of the project were the ability to quickly learn the distinguishing characteristics of novel objects, searching images for the object as the robot drove a search pattern, identifying people near the robot for safe operations, correctly identify the object among distractors, and localizing the object for retrieval. The classifier used to identify the objects will be discussed, including an analysis of its performance, and an overview of the entire system architecture presented. A discussion of the robot's performance in the competition will demonstrate the system's successes in real-world testing.

关键词： CANINE object recognition computer vision unmanned ground vehicle autonomy

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Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

Remotely controlling of mobile robots using gesture captured...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Hsu, Roy Chaoming Jian, Jhih-Wei Lin, Chih-Chuan Lai, Chien-Hung Liu, Cheng-Ting Dept Elect Engn 300 Syuefu Rd Chiayi 60004 Taiwan Dept Comp Sci Informat Engn Chiayi 60004 Taiwan

ISBN: (纸本)9780819494351

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.

关键词： Robot remote control Kinect sensor gesture recognition back propagation neural network

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Method and Application of Active Visual Tracking Based on Illumination Invariants

Method and Application of Active Visual Tracking Based on Il...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Su Jiea Yin Gui-sheng Wang Lei Sun Yong-qian Liu Zheng Harbin Univ Sci & Technol 52 Xuefu Rd Harbin 150080 Peoples R China Harbin Engn Univ Harbin 150001 Peoples R China

ISBN: (纸本)9780819494351

With the development of the application of visual tracking technology, the performance of visual tracking algorithm is important. Due to many kinds of voice, robust of tracking algorithm is bad. To improve identification rate and track rate for quickly moving target, expand tracking scope and lower sensitivity to illumination varying, an active visual tracking system based on illumination invariants is proposed. Camera motion pre-control method based on particle filter pre-location is used to improve activity and accuracy of track for quickly moving target by forecasting target position and control camera joints of Tilt, Pan and zoom. Pre-location method using particle sample filter according to illumination invariants of target is used to reduce the affect of varying illumination during tracking moving target and to improve algorithm robust. Experiments in intelligent space show that the robust to illumination vary is improved and the accuracy is improved by actively adjust PTZ parameters.

关键词： computer visual Active Tracking Particle Filter Model Illumination Invariants Robust

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Panoramic Stereo Sphere vision

Panoramic Stereo Sphere Vision

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Feng, Weijia Zhang, Baofeng Roning, Juha Zong, Xiaoning Yi, Tian Tianjin Normal Univ Coll Comp & Informat Engn Tianjin Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Appl Complied In Tianjin Peoples R China Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9780819494351

Conventional stereo vision systems have a small field of view (FOV) which limits their usefulness for certain applications. While panorama vision is able to "see" in all directions of the observation space, scene depth information is missed because of the mapping from 3D reference coordinates to 2D panoramic image. In this paper, we present an innovative vision system which builds by a special combined fish-eye lenses module, and is capable of producing 3D coordinate information from the whole global observation space and acquiring no blind area 360 degrees x360 degrees panoramic image simultaneously just using single vision equipment with one time static shooting. It is called Panoramic Stereo Sphere vision (PSSV). We proposed the geometric model, mathematic model and parameters calibration method in this paper. Specifically, video surveillance, robotic autonomous navigation, virtual reality, driving assistance, multiple maneuvering target tracking, automatic mapping of environments and attitude estimation are some of the applications which will benefit from PSSV.

关键词： Panoramic Stereo Sphere vision fish-eye lens parameters calibration

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LABRADOR: A Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval

LABRADOR: A Learning Autonomous Behavior-based Robot for Ada...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Yamauchi, Brian Moseley, Mark Brookshire, Jonathan iRobot Corp 8 Crosby Dr Bedford MA 01730 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819494351

As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

关键词： CANINE mobile robotics computer vision machine learning navigation human-robot interaction

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