The proceedings contain 28 papers. The topics discussed include: geometric alignment method for large point cloud pairs using sparse overlap areas;a probabilistic approach for the reconstruction of polyhedral objects ...
The proceedings contain 28 papers. The topics discussed include: geometric alignment method for large point cloud pairs using sparse overlap areas;a probabilistic approach for the reconstruction of polyhedral objects using shape from shading technique;people detection in crowded scenes using active contour models;homography-based multiple-camera person-tracking;hybrid real-time tracking of non-rigid objects under occlusions;combining a modified vector field histogram algorithm and real-time image processing for unknown environment navigation;development of a vision system for an intelligent ground vehicle;construction engineering robot kit: warfighter experiment;convoy active safety technologies warfighter experiment II;locating and tracking objects by efficient comparison of real and predicted synthetic video imagery;and scene categorization with multi-scale category-specific visual words.
A mobile robot moving in an environment in which there are other moving objects and active agents, some of which may represent threats and some of which may represent collaborators, needs to be able to reason about th...
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We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of visionalgorithms,...
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ISBN:
(纸本)9781424439928
We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of visionalgorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data and a distributed visual landmark matching capability with geometric consistency verification. We apply these techniques to implement a tag-along robot, where a human operator leads the way and a robot autonomously follows. We show results for a real-time implementation of such a system with real field constraints on CPU power and network resources.
History shows that problems that cause human confusion often lead to inventions to solve the problems, which then leads to exploitation of the invention, creating a confusion-invention-exploitation cycle. Robotics, wh...
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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 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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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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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.
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