2,281 research outputs found
Information gain-based exploration using Rao-Blackwellized particle filters
No full textThis paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-Theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach
Fr. Burgard, A. Haverkamp, Fr. Irsigler, W. Reichert, eds. Hochfinanz im Westen des Reiches, 1150-1500
No full textKusman David. Fr. Burgard, A. Haverkamp, Fr. Irsigler, W. Reichert, eds. Hochfinanz im Westen des Reiches, 1150-1500. In: Revue belge de philologie et d'histoire, tome 77, fasc. 2, 1999. Histoire médiévale moderne et contemporaine - Meddeleewse, moderne en hedendaagse geschiedenis. pp. 657-660
Nonlinear Constraint Network Optimization for Efficient Map Learning
No full textLearning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we present a highly efficient maximum-likelihood approach that is able to solve 3-D and 2-D problems. Our approach addresses the so-called graph-based formulation of simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson's algorithm toward non-flat environments. It applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory. Furthermore, our approach is able to appropriately distribute the roll, pitch, and yaw error over a sequence of poses in 3-D mapping problems. We implemented our technique and compared it with multiple other graph-based SLAM solutions. As we demonstrate in simulated and real-world experiments, our method converges faster than the other approaches and yields accurate maps of the environment
Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops
No full textAcquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters. © 2005 IEEE
F. Burgard, A. Haverkamp, F. Irsigler et W. Reichert, éds., Hochfinanz im Westen des Reiches, 1150-1500 (Trierer Historische Forschungen, vol. 31), 1996
No full textHocquet Jean-Claude. F. Burgard, A. Haverkamp, F. Irsigler et W. Reichert, éds., Hochfinanz im Westen des Reiches, 1150-1500 (Trierer Historische Forschungen, vol. 31), 1996. In: Revue du Nord, tome 81, n°329, Janvier-mars 1999. pp. 192-193
Editorial: Shared Autonomy— Learning of Joint Action and Human-Robot Collaboration
No full textSchilling M, Burgard W, Muelling K, Wrede B, Ritter H. Editorial: Shared Autonomy- Learning of Joint Action and Human-Robot Collaboration. Frontiers in neurorobotics. 2019;13: 16
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