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Axel Rottmann

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5 papers

2 author rows

ICRA Conference 2009 Conference Paper

A probabilistic sonar sensor model for robust localization of a small-size blimp in indoor environments using a particle filter

Jörg Müller 0004
Axel Rottmann
Leonhard M. Reindl
Wolfram Burgard

In recent years, autonomous miniature airships have gained increased interest in the robotics community. This is due to their ability to move safely and hover for extended periods of time. The major constraints of miniature airships come from their limited payload which introduces substantial constraints on their perceptional capabilities. In this paper, we consider the problem of localizing a miniature blimp with lightweight ultrasound sensors. Since the opening angle of the sound cone emitted by a sonar sensor depends on the diameter of the membrane, small-size sonar devices introduce the problem of high uncertainty about which object has been perceived. We present a novel sensor model for ultrasound sensors with large opening angles that allows an autonomous blimp to robustly localize itself in a known environment using Monte Carlo localization. As we demonstrate in experiments with a real blimp, our novel sensor model outperforms a popular sensor model that has in the past been shown to work reliably on wheeled platforms.

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ICRA Conference 2009 Conference Paper

Adaptive autonomous control using online value iteration with gaussian processes

Axel Rottmann
Wolfram Burgard

In this paper, we present a novel approach to controlling a robotic system online from scratch based on the reinforcement learning principle. In contrast to other approaches, our method learns the system dynamics and the value function separately, which permits to identify the individual characteristics and is, therefore, easily adaptable to changing conditions. The major problem in the context of learning control policies lies in high-dimensional state and action spaces, that needs to be explored in order to identify the optimal policy. In this paper, we propose an approach that learns the system dynamics and the value function in an alternating fashion based on Gaussian process models. Additionally, to reduce computation time and to make the system applicable to online learning, we present an efficient sparsification method. In experiments carried out with a real miniature blimp we demonstrate that our approach can learn height control online. Further results obtained with an inverted pendulum show that our method requires less data to achieve the same performance as an off-line learning approach.

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IROS Conference 2007 Conference Paper

Autonomous blimp control using model-free reinforcement learning in a continuous state and action space

Axel Rottmann
Christian Plagemann
Peter Hilgers
Wolfram Burgard

In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to previous approaches, our method does not require sophisticated hand- tuned models, but rather learns the policy online, which makes the system easily adaptable to changing conditions. The blimp we apply our approach to is a small-scale vehicle equipped with an ultrasound sensor that measures its elevation relative to the ground. The major problem in the context of learning control policies lies in the high-dimensional state-action space that needs to be explored in order to identify the values of all state-action pairs. In this paper, we propose a solution to learning continuous control policies based on the Gaussian process model. In practical experiments carried out on a real robot we demonstrate that the system is able to learn a policy online within a few minutes only.

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IROS Conference 2007 Conference Paper

Learning maps in 3D using attitude and noisy vision sensors

Bastian Steder
Giorgio Grisetti
Slawomir Grzonka
Cyrill Stachniss
Axel Rottmann
Wolfram Burgard

In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp.

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AAAI Conference 2005 Conference Paper

Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting

Axel Rottmann
Cyrill Stachniss

Indoor environments can typically be divided into places with different functionalities like kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. This paper presents a supervised learning approach to label different locations using boosting. We train a classifier using features extracted from vision and laser range data. Furthermore, we apply a Hidden Markov Model to increase the robustness of the final classification. Our technique has been implemented and tested on real robots as well as in simulation. The experiments demonstrate that our approach can be utilized to robustly classify places into semantic categories. We also present an example of localization using semantic labeling.

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