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 ...
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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 s...
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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...
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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 a...
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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...
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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 ha...
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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.
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 activit...
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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 computervisiontechniques, 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.
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...
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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.
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 alternat...
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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.
The proceedings contain 33 papers. The topics discussed include: new design method for a hierarchical SVM-based classifier;eclectic theory of intelligentrobots;emerging directions in lunar and planetary robotics;robu...
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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 intelligentrobots;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.
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