@string{springerstaradvanced = "STAR Springer tracts in advanced robotics"}

@InProceedings{plagemann08ecml,
  title     = {Nonstationary Gaussian Process Regression using Point Estimates
    of Local Smoothness},
  author    = {Plagemann, C. and Kersting, K. and Burgard, W.},
  booktitle = {Proc.~of the European Conference on Machine Learning (ECML)},
  address   = {Antwerp, Belgium},
  year      = {2008},
  abstract  = {
Gaussian processes using nonstationary covariance functions are a
powerful tool for Bayesian regression with input-dependent
smoothness. A common approach is to model the local smoothness by a
latent process that is integrated over using Markov chain Monte Carlo
approaches. In this paper, we demonstrate that an approximation that
uses the estimated mean of the local smoothness yields good results
and allows one to employ efficient gradient-based optimization
techniques for jointly learning the parameters of the latent and the
observed processes. Extensive experiments on both synthetic and
real-world data, including challenging problems in robotics, show the
relevance and feasibility of our approach.
},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann08ecml.pdf}
}

@proceedings{burgard08ias,
  editor    = {Burgard, W. and Dillmann, R. and Plagemann, C. and 
    Vahrenkamp, N.},
  title     = {Proc. of the 10th International Conference on Intelligent
    Autonomous Systems, Baden-Baden, Germany, July 23-25, 2008},
  booktitle = {IAS},
  publisher = {IOS Press},
  year      = {2008},
  isbn      = {978-1-58603-887-8},
}

@InProceedings{plagemann08iros,
  title     = {Learning Predictive Terrain Models for Legged Robot Locomotion},
  author    = {Plagemann, C. and Mischke, S. and Prentice, S. and Kersting, K.
    and Roy, N. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
    Intelligent Robots and Systems (IROS)},
  address   = {Nice, France},
  year      = {2008},
  abstract  = {
Legged robots require the ability to build accurate models of their
environment in order to plan and execute their actions. We present a
novel, probabilistic terrain model based on Gaussian processes that
can be learned and updated efficiently using sparse approximation
techniques. The major benefit of our model is its ability to predict
elevations at unseen locations more reliably than alternative
approaches, while it also yields estimates of the predictive
uncertainties. In particular, our Gaussian process adapts its
covariance to the situation at hand, allowing more accurate inference
of terrain height at points that have not been directly observed. We
show how a conventional motion planner can use the learned terrain
model to to plan a path to a goal location, using a terrain-specific
cost model to accept or reject candidate footholds. In experiments
with a real quadruped robot equipped with a laser range finder, we
demonstrate the usefulness of our approach and discuss its benefits
compared to simpler terrain models such as elevations grids.
},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann08iros.pdf}
}

@InProceedings{pfaff08iros,
  title     = {Efficiently Learning High-dimensional Observation Models
    for Monte-Carlo Localization using Gaussian Mixtures},
  author    = {Pfaff, P. and Stachniss, C. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
    Intelligent Robots and Systems (IROS)},
  address   = {Nice, France},
  year      = {2008},
  abstract  = {
Whereas probabilistic approaches are a powerful tool for mobile robot
localization, they heavily rely on the proper definition of the
so-called observation model which defines the likelihood of an
observation given the position and orientation of the robot and the
map of the environment. Most of the sensor models for range sensors
proposed in the past either consider the individual beam measurements
independently or apply uni-modal models to represent the likelihood
function. In this paper we present an approach that learns
place-dependent sensor models for entire range scans using Gaussian
mixture models. To deal with the high dimensionality of the
measurement space, we utilize principle component analysis for
dimensionality reduction. In practical experiments carried out with
data obtained from a real robot we demonstrate that our model
substantially outperforms existing and popular sensor models.
},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/pfaff08iros.pdf}
}

@InProceedings{kretzschmar08iros,
  title     = {Estimating Landmark Locations from Geo-Referenced Photographs},
  author    = {Kretzschmar, H. and Stachniss, C. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
    Intelligent Robots and Systems (IROS)},
  address   = {Nice, France},
  year      = {2008},
  abstract  = {},
  OPTpdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/.pdf}
}

@InProceedings{sturm08rss,
  TITLE     = {Adaptive Body Scheme Models for Robust Robotic Manipulation},
  AUTHOR    = {Sturm, J. and Plagemann, C. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Zurich, Switzerland},
  YEAR      = {2008},
  MONTH     = {June},
  OPTPDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/sturm08rss.pdf},
  OPTABSTRACT  = {},
}

@InProceedings{stachniss08rss,
  TITLE     = {Gas Distribution Modeling Using Sparse Gaussian Process Mixture Models},
  AUTHOR    = {Stachniss, C. and Plagemann, C. and Lilienthal, A. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Zurich, Switzerland},
  YEAR      = {2008},
  MONTH     = {June},
  OPTPDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/stachniss08rss.pdf},
  OPTABSTRACT  = {},
}

@InProceedings{luber08rss,
  TITLE     = {Tracking and Classification of Dynamic Objects: An Unsupervised
    Learning Approach},
  AUTHOR    = {Luber, M. and Arras, K. and Plagemann, C. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Zurich, Switzerland},
  YEAR      = {2008},
  MONTH     = {June},
  OPTPDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/sturm08rss.pdf},
  OPTABSTRACT  = {},
}

@InProceedings{reiser08robotik,
  TITLE     = {Verteilte Software-Entwicklung in der Robotik - ein Integrations- und
    Testframework},
  AUTHOR    = {Reiser, U. and Mies, C. and Plagemann, C.},
  BOOKTITLE = {Robotik},
  ADDRESS   = {Munich, Germany},
  YEAR      = {2008},
  OPTMONTH     = {},
  ABSTRACT  = {Eine der größten Herausforderungen innerhalb der Robotik ist die Integration vieler, komplexer Hardware- und Softwarekomponenten zu einem robust funktionierenden Gesamtsystem. Neben den zahlreichen wissenschaftlichen Fragestellungen, die auf Systemebene zu lösen sind, hängt der Integrationserfolg insbesondere von der Lösung praktischer Probleme wie dem Zusammenspiel vieler Entwicklungspartner und der typischerweise stark limitierten Verfügbarkeit von Einzelkomponenten ab. Leistungsfähige Hardwarekomponenten, wie beispielsweise Mehrfingergreifer und Leichtbauarme, sind in der Regel Spezialanfertigungen und stehen daher nur wenigen Partnern innerhalb von Projekten zur Verfügung. In diesem Beitrag wird ein neues Integrations- und Testframework zur räumlich verteilten Forschung und Entwicklung an solchen Komponenten und integrierten Systemen vorgestellt. Entwickler können hierbei Beiträge zu einer Technologieplattform leisten, ohne ständigen, direkten Zugang zur Hardware besitzen zu müssen.},
  NOTE      = {In German},
}

@InProceedings{plagemann08icra,
  title     = {Monocular Range Sensing: A Non-Parametric Learning Approach},
  author    = {Plagemann, C. and Endres, F. and Hess, J. and Stachniss, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
  address   = {Pasadena, CA, USA},
  year      = {2008},
  abstract  = {For many applications, mobile robots need to estimate the geometry of their local surrounding area.  To do so, proximity sensor such as laser range finders or sonars are typically employed.  Cameras are a cheap and lightweight alternative to such sensors, but do not offer proximity information directly.  In this paper, we present a novel approach to learning the relationship between range measurements and visual features extracted from a single monocular camera image.  As the learning engine, we apply Gaussian processes, a non-parametric learning technique that not only yields the most likely range prediction corresponding to a certain visual input but also the predictive uncertainty.  This information, in turn, can be utilized in an extended grid-based mapping scheme to update a model of the environment more gently where the predictions are unreliable.  In practical experiments carried out with a mobile robot equipped with an omnidirectional camera system in different environments, we show that our system is able to predict range scans accurate enough to construct maps of the environment.},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/plagemann08icra.pdf}
}

@InProceedings{pfaff08icra,
  title     = {Gaussian Mixture Models for Probabilistic Localization},
  author    = {Pfaff, P. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
  address   = {Pasadena, CA, USA},
  year      = {2008},
  abstract  = {Range sensors have become popular for mobile robot localization since they directly measure the geometry of the local environment. In situations in which the robot operates close to obstacles or in highly cluttered environments, however, small changes in the pose of the robot can lead to completely different geometries measured by the range sensor. The resulting enormous variances in the likelihood of observations can lead to major problems in probabilistic approaches such as Monte Carlo localization as important hypotheses or particles might get lost which substantially decreases the robustness of such approaches. A common solution is to artificially smooth the likelihood function or to only integrate a small fraction of the measurements. In this paper we present a more fundamental and robust approach which models the likelihood function for single range measurements as a mixture of Gaussians. In practical experiments we compare our approach to previous methods and demonstrate that it provides a substantially more robust localization.},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/pfaff08icra.pdf}
}

@InProceedings{sturm08icra,
  title     = {Unsupervised Body Scheme Learning through Self-Perception},
  author    = {Sturm, J. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
  address   = {Pasadena, CA, USA},
  year      = {2008},
  abstract  = {In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera.  Our approach yields a compact Bayesian network for the robot's kinematic structure including the forward and inverse models relating action commands and body pose.  We propose to simultaneously learn local action models for all pairs of perceivable body parts from data generated through random ``motor babbling.''  From this repertoire of local models, we construct a Bayesian network for the full system using the pose prediction accuracy on a separate cross validation data set as the criterion for model selection.  The resulting model can be used to predict the body pose when no perception is available and allows for gradient-based posture control.  In experiments with real and simulated manipulator arms, we show that our system is able to quickly learn compact and accurate models and to robustly deal with noisy observations.},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/sturm08icra.pdf}
}

@InProceedings{meyerdelius07gfkl,
  title     = {A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems},
  author    = {Meyer-Delius, C. and Plagemann, C. and von~Wichert, G. and Feiten, W. and Lawitzky, G. and Burgard, W.},
  booktitle = {Proc.~of the 31st Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications (GfKl)},
  address   = {Freiburg, Germany},
  year      = {2007},
  abstract  = {Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult task. Interpretations may depend on a history of states and there may be more than one valid interpretation. We propose a model for spatio-temporal situations using hidden Markov models based on relational state descriptions, which are extracted from the estimated state of an underlying dynamic system. Our model covers concurrent situations, scenarios with multiple agents, and situations of varying durations. To evaluate the practical usefulness of our model, we apply it to the concrete task of online traffic analysis.},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/meyerdelius07gfkl.pdf}}

@InProceedings{pfaff07iros,
  title     = {Improved Likelihood Models for Probabilistic Localization based on Range Scans},
  author    = {Pfaff, P. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
               Intelligent Robots and Systems (IROS)},
  address   = {San Diego, CA, USA},
  year      = {2007},
  abstract  = {},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/pfaff07iros.pdf}
}

@InProceedings{rottmann07iros,
  title     = {Autonomous Blimp Control using Model-free Reinforcement Learning in a Continuous State and Action Space},
  author    = {Rottmann, A. and Plagemann, C. and Hilgers, P. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
               Intelligent Robots and Systems (IROS)},
  address   = {San Diego, CA, USA},
  year      = {2007},
  abstract  = {},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/rottmann07iros.pdf}
}

@InProceedings{grzonka07fsr,
  author =	 {Grzonka, S. and Plagemann, C. and Grisetti, G. and Burgard, W.},
  title =	 {Look-ahead Proposals for Robust Grid-based SLAM},
  booktitle =	 {Proc. of the International Conference on Field and Service Robotics (FSR)},
  year =	 {2007},
  month =        {July},
  ADDRESS =      {Chamonix, France},
  PDFURL       = {http://www.informatik.uni-freiburg.de/~plagem/bib/grzonka07fsr.pdf},
  ABSTRACT  =    {Simultaneous Localization and Mapping (SLAM) is one of the classical problems in mobile robotics. The task is to build a map of the environment using on-board sensors while at the same time localizing the robot relative to this map. Rao-Blackwellized particle filters have emerged as a powerful technique for solving the SLAM problem in a wide variety of environments. It is a well-known fact for sampling-based approaches that the choice of the proposal distribution greatly influences the robustness and efficiency achievable by the algorithm. In this paper, we present a significantly improved proposal distribution for grid-based SLAM, which utilizes whole sequences of sensor measurements rather than only the most recent one. We have implemented our system on a real robot and evaluated its performance on standard data sets as well as in hard outdoor settings with few and ambiguous features. Our approach improves the localization accuracy and the map quality. At the same time, it substantially reduces the risk of mapping failures.},
}

@InProceedings{plagemann07rss,
  TITLE     = {Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders},
  AUTHOR    = {Plagemann, C. and Kersting, K. and Pfaff, P. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Atlanta, Georgia, USA},
  YEAR      = {2007},
  MONTH     = {June},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann07rss.pdf},
  ABSTRACT  = {In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot's performance in a great variety of tasks including localization, tracking, and map building. In this paper, we present a novel probabilistic measurement model for range finders, called Gaussian Beam Processes, which treats the measurement modeling task as a nonparametric Bayesian regression problem and solves it using Gaussian processes. The major advantage of our approach lies in the smoothness of the resulting model which appropriately represents correlations between adjacent beams using covariance functions. Moreover, the Gaussian process treatment results in a sound probabilistic measurement model with a pool of well-established techniques for likelihood estimation and range prediction for an arbitrary number of beams. Experiments on real world and synthetic data show that Gaussian Beam Processes combine the advantages of two popular measurement models.},
}

@InProceedings{lang07rss,
  TITLE     = {Adaptive Non-Stationary Kernel Regression for Terrain Modeling},
  AUTHOR    = {Lang, T. and Plagemann, C. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Atlanta, Georgia, USA},
  YEAR      = {2007},
  MONTH     = {June},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/lang07rss.pdf},
  ABSTRACT  = {Three-dimensional digital terrain models are of fundamental importance in many areas such as the geo-sciences and outdoor robotics. Accurate modeling requires the ability to deal with a varying data density and to balance smoothing against the preservation of discontinuities. The latter is particularly important for robotics applications, as discontinuities that arise, for example, at steps, stairs, or building walls are important features for path planning or terrain segmentation tasks. In this paper, we present an extension of the well-established Gaussian process regression technique, that utilizes non-stationary covariance functions to locally adapt to the structure of the terrain data. In this way, we achieve strong smoothing in flat areas and along edges and at the same time preserve edges and corners. The derived model yields predictive height distributions for arbitrary locations of the terrain and therefore allows us to fill gaps in data and to perform conservative predictions in occluded areas.},
}

@InProceedings{kersting07icml,
  TITLE     = {Most Likely Heteroscedastic Gaussian Process Regression},
  AUTHOR    = {Kersting, K. and Plagemann, C. and Pfaff, P. and Burgard, W.},
  BOOKTITLE = {International Conference on Machine Learning (ICML)},
  ADDRESS   = {Corvallis, Oregon, USA},
  YEAR      = {2007},
  MONTH     = {March},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/kersting07icml.pdf},
  ABSTRACT  = {This paper presents a novel Gaussian process (GP) approach to regression with input-dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value. In contrast to Goldberg et al., however, we do not use a Markov chain Monte Carlo method to approximate the posterior noise variance but a most likely noise approach. The resulting model is easy to implement and can directly be used in combination with various existing extensions of the standard GPs such as sparse approximations. Extensive experiments on both synthetic and real-world data, including a challenging perception problem in robotics, show the effectiveness of most likely heteroscedastic GP regression.},
}

@Article{kersting07ar,
  TITLE     = {Learning to Transfer Optimal Navigation Policies},
  AUTHOR    = {Kersting, K. and Plagemann, C. and Cocora, A. and Burgard, W. and De Raedt, L.},
  JOURNAL   = {Advanced Robotics. Special Issue on Imitative Robots},
  YEAR      = {2007},
  VOLUME    = {21},
  NUMBER    = {9},
  MONTH     = {September},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/kersting07ar.pdf},
  ABSTRACT  = {Autonomous agents that act in the real world utilizing sensory input greatly rely on the ability to plan their actions and to transfer these skills across tasks. The majority of path planning approaches for mobile robots, however, solve the current navigation problem from scratch given the current and goal configuration of the robot. Consequently, these approaches yield highly efficient plans for the specific situation, but the computed policies typically do not transfer to other, similar tasks. In this paper, we propose to apply techniques from statistical relational learning to the path planning problem. More precisely, we propose to learn relational decision trees as abstract navigation strategies from example paths. Relational abstraction has several interesting and important properties. First, it allows a mobile robot to imitate navigation behavior shown by users or by optimal policies. Second, it yields comprehensible models of behavior. Finally, a navigation policy learned in one environment naturally transfers to unknown environments. In several experiments with real robots and in simulated runs, we demonstrate that our approach yields efficient navigation plans. We show that our system is robust against observation noise and can outperform hand-crafted policies.},
}

@InProceedings{plagemann07snowb,
  TITLE     = {Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics},
  AUTHOR    = {Plagemann, C. and Kersting, K. and Pfaff, P. and Burgard, W.},
  BOOKTITLE = {Snowbird learning workshop},
  ADDRESS   = {San Juan, Puerto Rico},
  YEAR      = {2007},
  MONTH     = {March},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann07snowb.pdf},
}

@InProceedings{plagemann07ijcai,
  TITLE     = {Efficient Failure Detection on Mobile Robots Using Particle Filters with Gaussian Process Proposals},
  AUTHOR    = {Plagemann, C. and Fox, D. and Burgard, W.},
  BOOKTITLE = {Proc.~of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI)},
  ADDRESS   = {Hyderabad, India},
  YEAR      = {2007},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann07ijcai.pdf},
  ABSTRACT  = {The ability to detect failures and to analyze their causes is one of the preconditions of truly autonomous mobile robots. Especially online failure detection is a complex task, since the effects of failures are typically difficult to model and often resemble the noisy system behavior in a fault-free operational mode. In this paper we present an approach that applies Gaussian process classification and regression techniques for learning highly effective proposal distributions of a particle filter that is applied to track the state of the system.  As a result, the efficiency and robustness of the state estimation process is substantially improved. In practical experiments carried out with a real robot we demonstrate that our system is capable of detecting collisions with unseen obstacles while at the same time estimating the changing point of contact with the obstacle.},
}


@InProceedings{lamon06iros,
 TITLE     = {Mapping with an Autonomous Car},
 AUTHOR    = {Lamon, P. and Stachniss, C. and Triebel, R. and Pfaff, P. and Plagemann, C. and Grisetti, G. and Kolsky, S. and Burgard, W. and Siegwart, R.},
 BOOKTITLE = {In IEEE/RSJ IROS 2006 Workshop: Safe Navigation in Open and Dynamic Environments},
 ADDRESS   = {Beijing, China},
 YEAR      = {2006},
 Abstract  = {In this paper, we present an approach towards mapping and safe navigation in real, large-scale environments with an autonomous car. The goal is to enable the car to autonomously navigate on roads while avoiding obstacles and while simultaneously learning an accurate three-dimensional model of the environment. To achieve these goals, we apply probabilistic state estimation techniques, network-based pose optimization, and a sensor-based traversability analysis approach. In order to achieve fast map learning, our system compresses the sensor data using multi-level surface maps. The overall system runs on a modified Smart car equipped with different types of sensors. We present several results obtained from extensive experiments which illustrate the capabilities of our vehicle.}
}

@article{cocora06ki,
  title     = {Learning Relational Navigation Policies},
  author    = {Cocora, A. and Kersting, K. and Plagemann, C. and Burgard, W. and De Raedt, L.},
  journal   = {KI - K{\"u}nstliche Intelligenz, Themenheft Lernen und Selbstorganisation von Verhalten},
  pages     = {12--18},
  year      = {2006},
  volume    = {3}
}

@InProceedings{cocora06iros,
  title     = {Learning Relational Navigation Policies},
  author    = {Cocora, A. and Kersting, K. and Plagemann, C. and Burgard, W. and De Raedt, L.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
               Intelligent Robots and Systems (IROS)},
  address   = {Beijing, China},
  year      = {2006},
  abstract  = {Navigation is one of the fundamental tasks for a mobile robot. The majority of path planning approaches has been designed to entirely solve the given problem from scratch given the current and goal configurations of the robot. Although these approaches yield highly efficient plans, the computed policies typically do not transfer to other, similar tasks. We propose to learn relational decision trees as abstract navigation strategies from example paths. Relational abstraction has several interesting and important properties. First, it allows a mobile robot to generalize navigation plans from specific examples provided by users or exploration. Second, the navigation policy learned in one environment can be transferred to unknown environments. In several experiments with real robots in a real environment and in simulated runs, we demonstrate the usefulness
of our approach.},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/cocora06iros.pdf}
}

@InProceedings{plagemann06euros,
  author =	 {Plagemann, C. and Stachniss, C. and Burgard, W.},
  title =	 {Efficient Failure Detection for Mobile Robots using
                  Mixed-Abstraction Particle Filters},
  editor =	 {H.I. Christensen},
  booktitle =	 {European Robotics Symposium 2006},
  publisher =	 {Springer-Verlag Berlin Heidelberg, Germany},
  year =	 2006,
  volume =	 {22},
  series =	 springerstaradvanced,
  pages =	 {93--107},
  isbn =	 {3-540-32688-X},
  pdfurl =       {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann06euros.pdf}
}

@InProceedings{plagemann05ams,
  author      = {Plagemann, C. and Burgard, W.},
  title       = {Sequential Parameter Estimation for Fault Diagnosis in Mobile Robots Using Particle Filters.},
  booktitle   = {Autonome Mobile Systeme 2005 (AMS)},
  year        = {2005},
  pages       = {197-202},
  publisher   = {Springer}
}

@inproceedings{plagemann05dagm,
  author    = {Plagemann, C. and M{\"u}ller, T. and Burgard, W.},
  title     = {Vision-Based 3D Object Localization Using Probabilistic
               Models of Appearance.},
  booktitle = {Pattern Recognition, 27th DAGM Symposium, Vienna, Austria},
  year      = {2005},
  pages     = {184-191},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {3663},
  editor    = {Walter G. Kropatsch and Robert Sablatnig and Allan Hanbury},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann05dagm.pdf}
}

@MastersThesis{plagemann04mastersThesis,
  author =	 {Plagemann, C.},
  title =	 {{A}nsichtsbasierte {E}rkennung und {L}okalisierung von {O}bjekten zur {I}nitialisierung eines {V}erfolgungsprozesses},
  school =	 {University of Karlsruhe, Department of Computer
                  Science and Fraunhofer Institute IITB, Karlsruhe},
  year =	 2004,
  note =	 {In German},
  pdfurl =       {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann04mastersThesis.pdf}
}