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Event-based particle filtering for robot self-localization

Event-based particle filtering for robot self-localization

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IEEE International Conference on robotics and Biomimetics

作者： David Weikersdorfer Jörg Conradt Department of Mathematics and Computer Science and part of the Intelligent Autonomous Systems Group Technical University Munich Germany Department of Electrical Engineering and head of the group Neuroscientific System Theory (NST) Technical University Munich Germany

ISBN: (纸本)9781467321259

We propose a novel algorithm for robot self-localization using an embedded event-based sensor. This sensor produces a stream of events at microsecond time resolution which only represents pixel-level illumination changes in a scene, as e.g. caused by perceived motion. This is in contrast to classical image sensors, which wastefully transmit redundant information at a much lower frame rate. Our method adapts the commonly used Condensation Particle Filter Tracker to such event-based sensors. It works directly with individual, highly ambiguous pixel-events and does not employ event integration over time. The lack of complete discrete sensory measurements is addressed by applying an exponential decay model for hypotheses likelihood computation. The proposed algorithm demonstrates robust performance at low computation requirements; turning it suitable for implementation in embedded hardware on small autonomous robots. We evaluate our algorithm in a simulation environment and with experimental recorded data.

关键词： Filtering using applying Particle filters Filtration Robot Event-based Simulated Environment embedded hardware Sensors (image) information redundancy Frame Rate Robust control temporal resolution Sensors

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Using mobile relays in multi-robot exploration

Using mobile relays in multi-robot exploration

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2011 Australasian Conference on robotics and Automation

作者： De Hoog, Julian Cameron, Stephen Jiménez-González, Adrian De-Dios, J. Ramiro Martínez Ollero, Anibal Department of Computer Science University of Oxford United Kingdom Robotics Vision and Control Group University of Sevilla Spain

ISBN: (纸本)9780980740424

Many robotics tasks require autonomous exploration by teams of robots. In difficult or large environments, communication drop-out complicates this task. Several approaches exist that aim to keep the team connected, but even so there is an inherent limit to the range that can be explored. In this paper we describe and examine Role-Based Exploration, an approach that uses mobile relays to ferry information back and forth within the team, and compare it to methods that do not. There are significant advantages in the use of such relays, such as improved coordination and responsiveness, and adaptability to unexpected communication dropout. The approaches are implemented and validated on a team of real robots.

关键词： Robots

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Supervised texture segmentation through a multi-level pixel-...

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IEEE International Conference on Image Processing

作者： Jaime Melendez Xavier Girones Domenec Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain

This paper presents a new, efficient technique for supervised texture segmentation based on a set of specifically designed filters and a multi-level pixel-based classifier. Filter design is carried out by means of a neural network, which is trained to maximize the filters' discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed by a classification scheme that utilizes multiple evaluation window sizes following a top-down approach, which iteratively refines the resulting segmentation. The proposed technique is compared to previous supervised texture segmenters by using both synthetic compositions and real outdoor textured images.

关键词： Filter banks Image segmentation Feature extraction Gabor filters Adaptive filters Conferences Support vector machines

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Multi-modal visual attention for robotics active vision systems - A reference architecture

Multi-modal visual attention for robotics active vision syst...

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AISB 2011 Symposium on Architectures for Active vision

作者： Hülse, Martin McBride, Sebastian Lee, Mark Department of Computer Science Intelligent Robotics Group Aberywtwyth University SY23 3DB United Kingdom

ISBN: (纸本)9781908187000

This work introduces an architecture for a robotic active vision system equipped with a manipulator that is able to integrate visual and non-visual (tactile) sensorimotor experiences. Inspired by the human vision system, we have implemented a strict separation of object location (where-data) and object features (what-data) in the visual data stream. This separation of what- and where-data has computational advantage but requires sequential fixation of visual cues in order to create and update a coherent view of the world. Hence, visual attention mechanisms must be put in place to decide which is the most task-relevant cue to fixate next. Regarding object manipulation many task relevant object properties (e.g. tactile feedback) are not necessarily related to visual features. Therefore, it is important that non-visual object features can influence visual *** present and demonstrate visual attention mechanisms for an active vision system that are modulated by visual and non-visual object features.

关键词： Manipulators

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Boosting Scalable Gradient Features for Adaptive Real-Time Tracking

Boosting Scalable Gradient Features for Adaptive Real-Time T...

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2011 IEEE International Conference on robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Dominik A. Klein Armin B. Cremers Intelligent Vision Systems Group Department of Computer Science IIIRheinische Friedrich-Wilhelms-Universit(a)t Bonn53117 BonnGermany

Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT1 as well as the PROST2 datasets.

关键词： Adaptation models Target tracking Computational modeling Shape Training Reliability Real time systems

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An accurate 3D feature tracking system with wide-baseline stereo smart cameras

An accurate 3D feature tracking system with wide-baseline st...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Dominik Rueß Kristian Manthey Ralf Reulke German AeroSpace Center Optical Information Systems Institute of Robotics and Mechatronics Berlin Germany Department of Computer Science Research and Lecture Group Computer Vision Humboldt-Universität zu Berlin Berlin Germany

A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.

关键词： Trajectory Three dimensional displays Cameras Smart cameras Estimation Image reconstruction Feature extraction

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Reports of the AAAI 2010 fall symposia

Reports of the AAAI 2010 fall symposia

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作者： Azevedo, Roger Biswas, Gautam Bohus, Dan Carmichael, Ted Finlayson, Mark A. Hadzikadic, Mirsad Havasi, Catherine Horvitz, Eric Kanda, Takayuki Koyejo, Oluwasanmi Lawless, William F. Lenat, Doug Meneguzzi, Felipe Mutlu, Bilge Oh, Jean Pirrone, Roberto Raux, Antoine Sofge, Donald A. Sukthankar, Gita Van Durme, Benjamin Yorke-Smith, Neil Department of Educational and Counseling Psychology McGill University Canada Center for Intelligent Systems Vanderbilt University United States Microsoft Research Redmond United States Department of Software and Information Systems University of North Carolina Charlotte United States Complex Systems Institute United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Media Lab. Massachusetts Institute of Technology United States ATR Intelligent Robotics and Communication Laboratories Kyoto Japan Department of Electrical and Computer Engineering University of Texas Austin United States Department of Mathematics Paine College Augusta GA United States Cycorp Austin TX United States Robotics Institute Carnegie Mellon University United States Department of Computer Sciences University of Wisconsin Madison United States University of Palermo Italy Honda Research Institute United States Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory Washington DC United States Department of Electrical Engineering and Computer Science University of Central Florida United States Department of Computer Science Johns Hopkins University United States American University of Beirut Lebanon SRI International's Artificial Intelligence Center United States

The Association for the Advancement of Artificial Intelligence (AAAI) presented the 2010 Fall Symposium Series on November 11-13, 2010. The eight symposia included Cognitive and Metacognitive Educational Systems, Commonsense Knowledge, Complex Adaptive Systems: Resilience, Robustness, and Evolvability, Computational Models of Narrative, Dialog with Robots, Manifold Learning and Its Applications, Proactive Assistant Agents and Quantum Informatics for Cognitive, Social, and Semantic Processes. Cognitive and Metacognitive Educational Systems aimed to provide a comprehensive definition of metacognitive educational systems that is inclusive of the theoretical, architectural, and educational aspects of this field. The AAAI Commonsense Knowledge Fall Symposium had the goal of bringing together the diverse elements of this community whose work benefits from or contributes to the representation of general knowledge about the world. One of the specific goals of Proactive symposium was to gather the researchers from various projects in assistant agents to share their wisdom in retrospect.

关键词： Semantics

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The logic of robotics inspired biology

The logic of robotics inspired biology

引用

International Symposium on AI Inspired Biology, AIIB 2010 - A Symposium at the AISB 2010 Convention

作者： Hülse, Martin Lee, Mark Intelligent Robotics Group Department of Computer Science Aberywtwyth University SY23 3DB United Kingdom

ISBN: (纸本)1902956923

Biologically inspired robotics is a well known approach for the design of autonomous intelligent robot systems. Very often it is assumed that biologically inspired models successfully implemented on robots offer new scientific knowledge for biology too. In other words, robots experiments serving as a replacement for the biological system under investigation are assumed to provide new scientific knowledge for biology. This article is a critical investigation of this assumption. We begin by clarifying what we mean by "new scientific knowledge." Following Karl Popper's work the The Logic of Scientific Discovery we conclude that in general robotic experiments serving as replacement for biological systems can never directly deliver any new scientific knowledge for biology. We further argue that there is no formal guideline which defines the level of "biological plausibility" for biologically inspired robot implementations. Therefore, there is no reason to prefer some kind of robotic setup before others. Any claimed relevance for biology, however, is only justified if results from robotic experiments are translated back into new models and hypotheses amenable to experimental tests within the domain of biology. This translation "back" into biology is very often missing and we will discuss popular robotics frameworks in the context of Brain Research, Cognitive science and Developmental robotics in order to highlight this issue. Nonetheless, such frameworks are valuable and important, like pure mathematics, because they might lead to new formalisms and methods which in future might be essential for gaining new scientific knowledge if applied in biology. No one can tell, if and which of the current robotics frameworks will provide these new scientific tools. What we can already say-the main message of this article-is that robot systems serving as a replacement for biological systems won't be sufficient for the test of biological models, i.e. gaining new scientific knowledge i

关键词： intelligent robots

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On adapting pixel-based classification to unsupervised texture segmentation

On adapting pixel-based classification to unsupervised textu...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Melendez, Jaime Puig, Domenec Angel Garcia, Miguel Intelligent Robotics and Computer Vision Group Rovira I Virgili University Dept. of Computer Science and Mathematics Av. Paisos Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of-the-art unsupervised texture segmenters are also provided. © 2010 IEEE.

关键词： Clustering algorithms

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Robust color image segmentation through tensor voting

Robust color image segmentation through tensor voting

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International Conference on Pattern Recognition

作者： Moreno, Rodrigo Garcia, Miguel Angel Puig, Domenec Rovira I Virgili University Intelligent Robotics and Computer Vision Group Dept. of Computer Science and Mathematics Av. Països Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homogeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art. © 2010 IEEE.

关键词： Pixels

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