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intelligent robots and computer vision XXVI: algorithms and techniques

作者： Theisen, Bernard L. U.S. Army TARDEC 6501 E. Eleven Mile Road Warren MI 48397-5000 United States

The intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. 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 16 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 nearly 70 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. © 2009 SPIE-IS&T.

关键词： Competition

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Construction engineering robot kit: Warfighter experiment

Construction engineering robot kit: Warfighter experiment

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intelligent robots and computer vision XXVI: algorithms and techniques

作者： Theisen, Bernard L. Richardson, Paul U.S. Army TARDEC 6501 E. Eleven Mile Road Warren MI 48397-5000 United States University of Michigan-Dearborn 4901 Evergreen Road Dearborn MI 48128 United States

The Construction Equipment Robot Kit (CERK) Warfighter Experiment was conducted March 17-28, 2008 at Fort Leonard Wood, Missouri to assess if robotic construction equipment systems are an operationally effective and suitable means to support hasty route clearance and route remediation operations. This paper will present the findings of the Soldier testing of two different common-off-the-self (COTS) robotic kits installed on two different pieces of field construction equipment through a variety of operational vignettes. These vignettes were preformed during both day and night operations, with and without all weather gear on by Soldiers that are experienced operators of construction equipment. This paper will go into detailed analysis of the communications systems used between the robots and their operator control units (OCU) and the need to improve communications of both robotic kits. The results will show that these operations are possible to accomplish while removing the Soldier from harms way. © 2009 SPIE-IS&T.

关键词： robots

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Development of a vision system for an intelligent ground vehicle

Development of a vision system for an intelligent ground veh...

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intelligent robots and computer vision XXVI: algorithms and techniques

作者： Nagel, Robert L. Perry, Kenneth Stone, Robert B. McAdams, Daniel A. Design Eng. Lab Missouri Univ. of Sci. and Tech. 1870 Miner Circle Rolla MO 65409 United States Mech. Eng. Dept. Texas A and M Univ. Eng./Physics Bldg. College Station TX 77843 United States

The development of a vision system for an autonomous ground vehicle designed and constructed for the intelligent Ground Vehicle Competition (IGVC) is discussed. The requirements for the vision system of the autonomous vehicle are explored via functional analysis considering the flows (materials, energies and signals) into the vehicle and the changes required of each flow within the vehicle system. Functional analysis leads to a vision system based on a laser range finder (LIDAR) and a camera. Input from the vision system is processed via a ray-casting algorithm whereby the camera data and the LIDAR data are analyzed as a single array of points representing obstacle locations, which for the IGVC, consist of white lines on the horizontal plane and construction markers on the vertical plane. Functional analysis also leads to a multithreaded application where the ray-casting algorithm is a single thread of the vehicle's software, which consists of multiple threads controlling motion, providing feedback, and processing the data from the camera and LIDAR. LIDAR data is collected as distances and angles from the front of the vehicle to obstacles. Camera data is processed using an adaptive threshold algorithm to identify color changes within the collected image;the image is also corrected for camera angle distortion, adjusted to the global coordinate system, and processed using least-squares method to identify white boundary lines. Our IGVC robot, MAX, is utilized as the continuous example for all methods discussed in the paper. All testing and results provided are based on our IGVC robot, MAX, as well. © 2009 SPIE-IS&T.

关键词： Cameras

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Scene categorization with multi-scale category-specific visual words

Scene categorization with multi-scale category-specific visu...

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intelligent robots and computer vision XXVI: algorithms and techniques

作者： Qin, Jianzhao Yung, Nelson H. C. Laboratory for Intelligent Transportation Systems Research Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Road Hong Kong Hong Kong

In this paper, we propose a scene categorization method based on multi-scale category-specific visual words. The proposed method quantizes visual words in a multi-scale manner which combines the global-feature-based and local-feature-based scene categorization approaches into a uniform framework. Unlike traditional visual word creation methods which quantize visual words from the whole training images without considering their categories, we form visual words from the training images grouped in different categories then collate the visual words from different categories to form the final codebook. This category-specific strategy provides us with more discriminative visual words for scene categorization. Based on the codebook, we compile a feature vector that encodes the presence of different visual words to represent a given image. A SVM classifier with linear kernel is then employed to select the features and classify the images. The proposed method is evaluated over two scene classification datasets of 6,447 images altogether using 10-fold cross-validation. The results show that the classification accuracy has been improved significantly comparing with the methods using the traditional visual words. And the proposed method is comparable to the best results published in the previous literatures in terms of classification accuracy rate and has the advantage in terms of simplicity. © 2009 SPIE-IS&T.

关键词： Classification (of information)

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Kratos: Princeton University's entry in the 2008 intelligent ground vehicle competition

Kratos: Princeton University's entry in the 2008 intelligent...

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intelligent robots and computer vision XXVI: algorithms and techniques

作者： Baldassano, Christopher A. Franken, Gordon H. Mayer, Jonathan R. Saxe, Andrew M. Yu, Derrick D. Princeton University School of Engineering and Applied Science Princeton NJ United States

In this paper we present Kratos, an autonomous ground robot capable of static obstacle field navigation and lane following. A sole color stereo camera provides all environmental data. We detect obstacles by generating a 3D point cloud and then searching for nearby points of differing heights, and represent the results as a cost map of the environment. For lane detection we merge the output of a custom set of filters and iterate the RANSAC algorithm to fit parabolas to lane markings. Kratos' state estimation is built on a square root central difference Kalman filter, incorporating input from wheel odometry, a digital compass, and a GPS receiver. A 2D A* search plans the straightest optimal path between Kratos' position and a target waypoint, taking vehicle geometry into account. A novel C++ wrapper for Carnegie Mellon's IPC framework provides flexible communication between all services. Testing showed that obstacle detection and path planning were highly effective at generating safe paths through complicated obstacle fields, but that Kratos tended to brush obstacles due to the proportional law control algorithm cutting turns. In addition, the lane detection algorithm made significant errors when only a short stretch of a lane line was visible or when lighting conditions changed. Kratos ultimately earned first place in the Design category of the intelligent Ground Vehicle Competition, and third place overall. © 2009 SPIE-IS&T.

关键词： Kalman filters

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Autonomous altitude estimation of a UAV using a single onboard camera

Autonomous altitude estimation of a UAV using a single onboa...

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2009 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Anoop Cherian Jon Andersh Vassilios Morellas Nikolaos Papanikolopoulos Bernard Mettler Department of Computer Science University of Minnesota Minneapolis MN USA Department of AeroSpace Engineering University of Minnesota Minneapolis MN USA

Autonomous estimation of the altitude of an Unmanned Aerial Vehicle (UAV) is extremely important when dealing with flight maneuvers like landing, steady flight, etc. vision based techniques for solving this problem have been underutilized. In this paper, we propose a new algorithm to estimate the altitude of a UAV from top-down aerial images taken from a single on-board camera. We use a semi-supervised machine learning approach to solve the problem. The basic idea of our technique is to learn the mapping between the texture information contained in an image to a possible altitude value. We learn an over complete sparse basis set from a corpus of unlabeled images capturing the texture variations. This is followed by regression of this basis set against a training set of altitudes. Finally, a spatio-temporal Markov Random Field is modeled over the altitudes in test images, which is maximized over the posterior distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a laboratory setting with a real helicopter and is found to provide promising results with sufficiently fast turnaround time.

关键词： Unmanned aerial vehicles Machine learning Helicopters intelligent robots USA Councils Robot vision systems Smart cameras computer science Machine learning algorithms Markov random fields

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3D feature based mapping towards mobile robots' enhanced performance in rescue missions

3D feature based mapping towards mobile robots' enhanced per...

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2009 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Paloma de la Puente Diego Rodriguez-Losada Alberto Valero Fernando Matia Intelligent Control Group Universidad Politecnica de Madrid Madrid Spain

This paper presents a feature based 3D mapping approach with regard to obtaining compact models of semi-structured environments such as partially destroyed buildings where mobile robots are to carry out rescue activities. To gather the 3D data, we use a laser scanner, employing a nodding data acquisition system mounted on both real and simulated robots. Our segmentation algorithm comes up from the integration of computer vision techniques, allowing for a fast separation of points corresponding to different, not necessarily planar, surfaces. The subsequent extraction of geometrical features out of each region's points is done by means of least-squares fitting. A Maximum Incremental Probability algorithm formulated upon the Extended Kalman Filter provides 6D localization and produces a map of planar patches with a convex-hull based representation. Scenarios from the Unified System for Automation and Robot Simulation (USARSim), including world models from past RoboCup Rescue editions' arenas, have been utilized to conduct some of our experiments.

关键词： Mobile robots Robotics and automation intelligent robots Laser modes Feature extraction Solid modeling Iterative algorithms Iterative closest point algorithm Simultaneous localization and mapping Noise measurement

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Simultaneous object recognition and position tracking for robotic applications

Simultaneous object recognition and position tracking for ro...

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International Conference on Mechatronics (ICM)

作者： Dimitris Aristos Spyros Tzafestas Intelligent Robotics and Automation Laboratory School of Electrical and Computer Engineering National and Technical University of Athens Athens Greece

Object recognition techniques are well known in the field of machine vision, and aim at the classification of certain observed rigid objects based on the information acquired by a specific sensor. These techniques can either be performed in the 2D image space by simply applying suitable image processing algorithms, or in the real world 3D space by performing surface reconstruction of the object's surface and comparing it with a database of known CAD data. This paper presents a combination of techniques which not only recognize and categorize the observed rigid objects but also compute their 3D pose (position and orientation) with respect an industrial robot's workspace frame. This task is very useful in the field of robotics as it allows an industrial robot to simultaneously recognize and follow the displacements of a specific rigid object, among other objects that may co-exist in the same environment.

关键词： Object recognition Service robots Orbital robotics Surface reconstruction Robot sensing systems Machine vision Image processing Image reconstruction Image databases Object oriented databases

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An intelligent mobile robot navigation technique using RFID technology

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2008年第9期57卷 1908-1917页

作者： Gueaieb, Wail Miah, Md. Suruz Univ Ottawa Sch Informat Technol & Engn MIRaM Lab Ottawa ON K1N 6N5 Canada

This paper presents an innovative mobile robot navigation technique using Radio Frequency IDentification (RFID) technology. Navigation based on processing some analog features of an RFID signal is a promising alternative to different types of navigation methods in the state of the art. The main idea is to exploit the ability of a mobile robot to navigate a priori unknown environments without a vision system and without building an approximate map of the robot workspace, as is the case in most other navigation algorithms. This paper discusses how this is achieved by placing RFID tags in the 3-D space so that the lines linking their projections on the ground define the "free ways" along which the robot can (or is desired to) move. The suggested algorithm is capable of reaching a target point in its a priori unknown workspace, as well as tracking a desired trajectory with a high precision. The proposed solution offers a modular, computationally efficient, and cost-effective alternative to other navigation techniques for a large number of mobile robot applications, particularly for service robots, such as, for instance, in large offices and assembly lines. The effectiveness of the proposed approach is illustrated through a number of computer simulations considering testbeds of various complexities.

关键词： fuzzy logic mobile robots navigation position control Radio Frequency IDentification (RFID) robot sensing systems

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Proceedings of SPIE - intelligent robots and computer vision XXV: algorithms, techniques, and Active vision

Proceedings of SPIE - Intelligent Robots and Computer Vision...

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intelligent robots and computer vision XXV: algorithms, techniques, and Active vision

ISBN: (纸本)9780819469243

The proceedings contain 33 papers. The topics discussed include: new design method for a hierarchical SVM-based classifier;eclectic theory of intelligent robots;emerging directions in lunar and planetary robotics;robust pedestrian detection and tracking in crowded scenes;multiple pedestrian detection using IR LED stereo camera;a fixed-point kanade lucas tomasi tracker implementation for smart cameras;detection and tracking using multi-color target models;a real time vehicle tracking system for an outdoor mobile robot;an occlusion robust likelihood integration method for multi-camera people head tracking;articulated motion analysis via axis-based representation;an automatic quality system for injection molding;a concept for ubiquitous robotics in industrial environment;and dynamic template size control in digital image correlation based strain measurements.

关键词： computer vision

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