The Experts below are selected from a list of 7614 Experts worldwide ranked by ideXlab platform
Armin B Cremers - One of the best experts on this subject based on the ideXlab platform.
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Visual Landmark generation and redetection with a single feature per frame
International Conference on Robotics and Automation, 2010Co-Authors: Simone Frintrop, Armin B CremersAbstract:In this paper we show that Visual Landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build Landmarks. We show that we can represent a large environment with a few salient Landmarks and that a large percentage of these Landmarks is robustly redetectable from different viewpoints.
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ICRA - Visual Landmark generation and redetection with a single feature per frame
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Simone Frintrop, Armin B CremersAbstract:In this paper we show that Visual Landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build Landmarks. We show that we can represent a large environment with a few salient Landmarks and that a large percentage of these Landmarks is robustly redetectable from different viewpoints.
Gabriele Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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Real-time optical SLAM-based mosaicking for unmanned underwater vehicles
Intelligent Service Robotics, 2012Co-Authors: Fausto Ferreira, G. Veruggio, Massimo Caccia, Gabriele BruzzoneAbstract:This article discusses the possibility of building in real-time a mosaic of the seafloor relying on a simultaneous localization and mapping (SLAM) framework. The goal is to provide an unmanned underwater vehicle with a relatively rough Visual map of the seafloor to support basic navigation and context awareness. To achieve that goal, an accurate estimation of the location of the Visual Landmarks and, in particular, the correct data association when a Visual Landmark is re-visited by the vehicle are the crucial points. Instead of using a global mosaic, thiswork uses the combination of a set of local mosaics constructed in the vicinity of the SLAM Visual Landmarks. The contributions of this article are mainly the use of SURF features, the local mosaics approach and the real-time capability. The use of SURF features allows eliminating false positives in the data association of SLAM Visual Landmarks. The local mosaics approach is an effective way of correcting the effects of the drift on the mosaic in real time. The main contribution is the real-time capability as it will be seen. The algorithm was tested using a batch of experimental data in typical operating conditions and the results prove the effectiveness of the approach.
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ICRA - An online SLAM-based mosaicking using local maps for ROVs
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Fausto Ferreira, G. Veruggio, Massimo Caccia, Gabriele BruzzoneAbstract:This article discusses the possibility of building online a mosaic of the seafloor relying on a SLAM framework. The goal is to provide the ROV operator with an approximated seafloor's Visual map relatively rough. In order to have that map, it is important to get an accurate estimate of the location of the Visual Landmarks and, in particular, a correct data association when a Visual Landmark is re-visited by the vehicle. The proposed approach uses the combination of a set of local mosaics constructed in the proximity of the SLAM Visual Landmarks instead of using a global mosaic. The algorithm was tested using a batch of experimental data in typical operating conditions and the results show the effectiveness of the approach.
Lakhmi C. Jain - One of the best experts on this subject based on the ideXlab platform.
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Application of neural processing paradigm in Visual Landmark recognition and autonomous robot navigation
Neural Computing and Applications, 2009Co-Authors: Lakhmi C. JainAbstract:This article addresses the issue of Visual Landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human Visual system and its navigational ability. A feedforward–feedbackward architecture has been developed for recognising Visual Landmarks in real time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human Visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches towards object recognition in computer vision. The architecture mimics the pre-attentive and attentive stages in the context of object recognition, embedding neural network processing paradigm into a computational template-matching approach in computer vision. The real-time Landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for optimal computational resource allocation, focusing only on the regions of interest to address the computational restrictive nature of current computer processing power. Similarly, the recognition of Visual Landmarks in both clean and cluttered backgrounds is implemented in the attentive stage by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the architecture. Furthermore, it also incorporates both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D Landmark, which is partially occluded by adjacent features in the surroundings. The results show that the architecture is able to recognise objects in cluttered backgrounds using real-images in both indoor and outdoor scenes. Furthermore, the architecture application in autonomous robot navigation has been demonstrated through a number of real-time trials in both indoor and outdoor environments.
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DICTA - A Biological Inspired Visual Landmark Recognition Architecture
2009 Digital Image Computing: Techniques and Applications, 2009Co-Authors: Lakhmi C. JainAbstract:An architecture that is inspired by a human’s capability to autonomously navigate an environment based on Visual Landmark recognition is presented. It consists of pre-attentive and attentive stages that allow Visual Landmarks to be recognized reliably under both clean and cluttered backgrounds. The pre-attentive stage provides an efficient means for real-time image processing by selectively focusing on regions of interest within input images. The attentive stage has a memory feedback modulation mechanism that allows Visual knowledge of Landmarks in the memory to interact and guide different stages in the architecture for efficient feature extraction and Landmark recognition. The results show that the architecture is able to reliably recognise both occluded and non-occluded Visual Landmarks in complex backgrounds.
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A Visual Landmark Recognition System for Autonomous Robot Navigation
2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web, 2006Co-Authors: Quoc V. Do, Lakhmi C. JainAbstract:This paper presents a vision system for autonomously guiding a robot along a known route using a single CCD camera. The prominent feature of the system is the real-time recognition of shape-based Visual Landmarks in cluttered backgrounds, using a memory feedback modulation (MFM) mechanism, which provides a means for the knowledge from the memory to interact and enhance the earlier stages in the system. Its feasibility in autonomous robot navigation is demonstrated in both indoor and outdoor experiments using a vision-based navigating vehicle.
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CIMCA/IAWTIC - A Visual Landmark Recognition System for Autonomous Robot Navigation
2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web, 2006Co-Authors: Lakhmi C. JainAbstract:This paper presents a vision system for autonomously guiding a robot along a known route using a single CCD camera. The prominent feature of the system is the real-time recognition of shape-based Visual Landmarks in cluttered backgrounds, using a memory feedback modulation (MFM) mechanism, which provides a means for the knowledge from the memory to interact and enhance the earlier stages in the system. Its feasibility in autonomous robot navigation is demonstrated in both indoor and outdoor experiments using a vision-based navigating vehicle.
Simone Frintrop - One of the best experts on this subject based on the ideXlab platform.
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Visual Landmark generation and redetection with a single feature per frame
International Conference on Robotics and Automation, 2010Co-Authors: Simone Frintrop, Armin B CremersAbstract:In this paper we show that Visual Landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build Landmarks. We show that we can represent a large environment with a few salient Landmarks and that a large percentage of these Landmarks is robustly redetectable from different viewpoints.
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ICRA - Visual Landmark generation and redetection with a single feature per frame
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Simone Frintrop, Armin B CremersAbstract:In this paper we show that Visual Landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build Landmarks. We show that we can represent a large environment with a few salient Landmarks and that a large percentage of these Landmarks is robustly redetectable from different viewpoints.
Se Young Oh - One of the best experts on this subject based on the ideXlab platform.
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SMC - Efficient Visual salient object Landmark extraction and recognition
2011 IEEE International Conference on Systems Man and Cybernetics, 2011Co-Authors: Lae-kyoung Lee, Su-yong An, Se Young OhAbstract:This article presents an efficient Visual Landmark extraction and recognition method that can autonomously and rapidly detect Visual features such as objects or groups of small objects, and that can be applied to Visual object recognition based SLAM and navigation in indoor/large environments using a monocular/omnidirection vision system. Our method consists of two-stage: (1) we autonomously extract object regions with modified fuzzy object segmentation. We generate a saliency map of the scene based on Modified Phase spectrum of Fourier Transform (mPFT) and extract the final salient object Landmark with weighted combination of candidate of objects and saliency map. (2) Using these result, we register current objects as Visual Landmark and then recognize the current image the scale invariant feature transform (SIFT) - based recognition with probabilistic voting. In experiments results in real indoor and large hall environments, the proposed method was simpler and 10∼15% better performance in computation efficiency and successfully extracted salient object Landmark in complex environments with high recognition rates. The proposed algorithm can be easily implemented in real-time by reducing the number of objects considered.
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Efficient Visual salient object Landmark extraction and recognition
Conference Proceedings - IEEE International Conference on Systems Man and Cybernetics, 2011Co-Authors: Lae-kyoung Lee, Su-yong An, Se Young OhAbstract:This article presents an efficient Visual Landmark extraction and recognition method that can autonomously and rapidly detect Visual features such as objects or groups of small objects, and that can be applied to Visual object recognition based SLAM and navigation in indoor/large environments using a monocular/omnidirection vision system. Our method consists of two-stage: (1) we autonomously extract object regions with modified fuzzy object segmentation. We generate a saliency map of the scene based on Modified Phase spectrum of Fourier Transform (mPFT) and extract the final salient object Landmark with weighted combination of candidate of objects and saliency map. (2) Using these result, we register current objects as Visual Landmark and then recognize the current image the scale invariant feature transform (SIFT) - based recognition with probabilistic voting. In experiments results in real indoor and large hall environments, the proposed method was simpler and 10∼15% better performance in computation efficiency and successfully extracted salient object Landmark in complex environments with high recognition rates. The proposed algorithm can be easily implemented in real-time by reducing the number of objects considered. © 2011 IEEE.
