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Auflistung nach Autor:in "Burgard, Wolfram"

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  • Zeitschriftenartikel
    Die Freiburger „Stanford AI Class“ Erfahrung
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Burgard, Wolfram
  • Konferenzbeitrag
    Learning accurate three-dimensional models from range data using global constraints
    (Informatik 2005 – Informatik Live! Band 2, 2005) Burgard, Wolfram; Triebel, Rudolph
    Recently, the acquisition of three-dimensional maps from range scans acquired with mobile robots has become more and more popular. This is motivated by the fact that robots act in the three-dimensional world and several tasks such as path planning or localizing objects can be carried out more reliably using three-dimensional representations. Key questions in this context are how to reduce the complexity of the data and how to efficiently match scans. Man-made structures such as buildings typically contain many structures such as planes and corners that are parallel. In this presentation we describe recently developed techniques that take into account such constraints to better approximate the data by planes to compute more accurate registrations. For plane extraction we use a hierarchical version of the expectation maximization (EM) algorithm to simultaneously cluster the data points into planes and the planes into their corresponding main directions. The information about the main directions is incorporated in the maximization step to calculate the parameters of the individual planes. We present experimental results obtained with real data and in simulation which demonstrate that our algorithm can accurately extract planes and their orientation from range data. Further results illustrate that our approach yields more accurate planes than the standard EM technique. Additionally, we present an approach that improves the registration process of three-dimensional range scans by introducing global constraints between the poses from which the scans were taken. Our approach minimizes not only the distance between scans, but also the distance of edges extracted from the scans to planes that are supported by the edges. This seriously decreases the required overlap between scans and in this way allows to reduce the number of data points in the model. We present experimental results illustrating that the global cons- traints allow to learn more accurate models even when there only is a small overlap between the scans.
  • Zeitschriftenartikel
    Probabilistic State Estimation Techniques for Autonomous and Decision Support Systems
    (Informatik-Spektrum: Vol. 34, No. 5, 2011) Burgard, Wolfram; Fox, Dieter; Thrun, Sebastian
    One of the ultimate goals of the field of artificial intelligence and robotics is to develop systems that assist us in our everyday lives by autonomously carrying out a variety of different tasks. To achieve this and to generate appropriate actions, such systems need to be able to accurately interpret their sensory input and estimate their state or the state of the environment to be successful. In recent years, probabilistic approaches have emerged as a key technology for these problems. In this article, we will describe state-of-the-art solutions to challenging tasks from the area of mobile robotics, autonomous cars, and activity recognition, which are all based on the paradigm of probabilistic state estimation.
  • Konferenzbeitrag
    Robotics: Probabilistic methods for state estimation and control
    (INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt, 2013) Burgard, Wolfram; Stachniss, Cyrill
    Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. Probabilistic approaches have been discovered as one of the most powerful ways to address highly relevant problems in mobile robotics, including robot state estimation and localization. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. Robots are inherently uncertain about the state of their environments. Uncertainty arises from sensor limitations, noise, and the fact that most interesting environments are - to a certain degree - unpredictable. When “guessing” a quantity from sensor data, the probabilistic approach computes a probability distribution over what might be the case in the world, instead of generating a single “best guess” only. As a result, a robot using probabilistic methods can gracefully recover from errors, handle ambiguities, and integrate sensor data in a consistent way.