Author name cluster

Patrick Pfaff

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

ICRA Conference 2013 Conference Paper

Probabilistic time-dependent models for mobile robot path planning in changing environments

Stefan Loibl
Daniel Meyer-Delius
Patrick Pfaff

In the context of mobile robot path planning, a common strategy is to assume that the world is static and rely on heuristic approaches and obstacle avoidance to deal with the changes in the environment. When planning, not taking the potential changes of the environment into account usually leads to poor performances. In this paper we propose a probabilistic model that explicitly characterizes the traversability of the environment as a stochastic process. Furthermore, we present a path planning approach that exploits this traversability information to compute paths that minimize the expected travel time of the robot. Experimental results show that by explicitly modeling and reasoning about changes in the environment path planning performance can be improved.

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IROS Conference 2012 Conference Paper

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Jörg Röwekämper
Christoph Sprunk
Gian Diego Tipaldi
Cyrill Stachniss
Patrick Pfaff
Wolfram Burgard

Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.

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ICRA Conference 2011 Conference Paper

Online generation of kinodynamic trajectories for non-circular omnidirectional robots

Christoph Sprunk
Boris Lau
Patrick Pfaff
Wolfram Burgard

This paper presents a novel approach to kino-dynamic trajectory generation for non-circular omnidirectional platforms that can be combined with existing path planners. We use quintic Bézier splines to specify position and orientation of the holonomic robot for every point in time. To fully exploit the capabilities of the holonomic robot we propose a novel path representation. It allows for continuous variation of path shapes in the spectrum between straight-line paths with turns on the spot and smooth paths with independent rotations and translations. Using this representation our method optimizes trajectories according to a user-defined cost function, considering the constraints of the platform. This way, it generates fast and efficient trajectories in an anytime fashion. The experiments carried out on an industrial robot show that our approach generates highly efficient and smooth motion trajectories that can be tracked with high precision and predictability. Furthermore, the system operates in real-world environments containing unmapped obstacles and narrow passages.

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IROS Conference 2008 Conference Paper

Efficiently learning high-dimensional observation models for Monte-Carlo localization using Gaussian mixtures

Patrick Pfaff
Cyrill Stachniss
Christian Plagemann
Wolfram Burgard

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.

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ICRA Conference 2008 Conference Paper

Gaussian mixture models for probabilistic localization

Patrick Pfaff
Christian Plagemann
Wolfram Burgard

One of the key tasks during the realization of probabilistic approaches to localization is the design of a proper sensor model, that calculates the likelihood of a measurement given the current pose of the vehicle and the map of the environment. In the past, range sensors have become popular for mobile robot localization since they directly measure distance. However, in situations in which the robot operates close to edges of obstacles or in highly cluttered environments, small changes in the pose of the robot can lead to large variations in the acquired range scans. If the sensor model used does not appropriately characterize the resulting fluctuations, the performance of probabilistic approaches may substantially degrade. 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 uses mixtures of Gaussians to model the likelihood function for single range measurements. In practical experiments we compare our approach to previous methods and demonstrate that it yields a substantially increase in robustness.

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IROS Conference 2007 Conference Paper

Efficient estimation of accurate maximum likelihood maps in 3D

Giorgio Grisetti
Slawomir Grzonka
Cyrill Stachniss
Patrick Pfaff
Wolfram Burgard

Learning maps is one of the fundamental tasks of mobile robots. 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 consider the problem of learning maps with mobile robots that operate in non-flat environments and apply maximum likelihood techniques to solve the graph-based SLAM problem. Due to the non-commutativity of the rotational angles in 3D, major problems arise when applying approaches designed for the two-dimensional world. The non-commutativity introduces serious difficulties when distributing a rotational error over a sequence of poses. In this paper, we present an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes. Our approach applies a variant of gradient descent to solve the error minimization problem. We implemented our technique and tested it on large simulated and real world datasets. We furthermore compared our approach to solving the problem by LU-decomposition. As the experiments illustrate, our technique converges significantly faster to an accurate map with low error and is able to correct maps with bigger noise than existing methods.

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IROS Conference 2007 Conference Paper

Improved likelihood models for probabilistic localization based on range scans

Patrick Pfaff
Christian Plagemann
Wolfram Burgard

Range sensors are popular for localization since they directly measure the geometry of the local environment. Another distinct benefit is their typically high accuracy and spatial resolution. It is a well-known problem, however, that the high precision of these sensors leads to practical problems in probabilistic localization approaches such as Monte Carlo localization (MCL), because the likelihood function becomes extremely peaked if no means of regularization are applied. In practice, one therefore artificially smoothes the likelihood function or only integrates a small fraction of the measurements. In this paper we present a more fundamental and robust approach, that provides a smooth likelihood model for entire range scans. Additionally, it is location-dependent. In practical experiments we compare our approach to previous methods and demonstrate that it leads to a more robust localization.

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ICML Conference 2007 Conference Paper

Most likely heteroscedastic Gaussian process regression

Kristian Kersting
Christian Plagemann
Patrick Pfaff
Wolfram Burgard

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.

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ICRA Conference 2007 Conference Paper

Towards Mapping of Cities

Patrick Pfaff
Rudolph Triebel
Cyrill Stachniss
Pierre Lamon
Wolfram Burgard
Roland Siegwart

Map learning is a fundamental task in mobile robotics because maps are required for a series of high level applications. In this paper, we address the problem of building maps of large-scale areas like villages or small cities. We present our modified car-like robot which we use to acquire the data about the environment. We introduce our localization system which is based on an information filter and is able to merge the information obtained by different sensors. We furthermore describe out mapping technique that is able to compactly model three-dimensional scenes and allows us efficient and accurate incremental map learning. We additionally apply a global optimization techniques in order to accurately close loops in the environment. Our approach has been implemented and deeply tested on a real car equipped with a series of sensors. Experiments described in this paper illustrate the accuracy and efficiency of the presented techniques.

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IROS Conference 2006 Conference Paper

Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing

Rudolph Triebel
Patrick Pfaff
Wolfram Burgard

To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete grid the height of the surface in the corresponding area. Whereas elevation maps provide a compact representation, they lack the ability to represent vertical structures or even multiple levels. In this paper, we propose a new representation denoted as multi-level surface maps (MLS maps). Our approach allows to store multiple surfaces in each cell of the grid. This enables a mobile robot to model environments with structures like bridges, underpasses, buildings or mines. Additionally, they allow to represent vertical structures. Throughout this paper we present algorithms for updating these maps based on sensory input, to match maps calculated from two different scans, and to solve the loop-closing problem given such maps. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach is well-suited for representing large-scale outdoor environments

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