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Sven Hellbach

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2 papers

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IROS Conference 2014 Conference Paper

Large scale place recognition in 2D LIDAR scans using Geometrical Landmark Relations

Marian Himstedt
Jan Frost
Sven Hellbach
Hans-Joachim Böhme
Erik Maehle

The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w. r. t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance of discriminative features within a bag-of-words (BOW) framework accompanied with approximate geometric verification. Though limiting the set of potential matches their performance crucially drops for increasing number of scans making them less appropriate for large scale environments. We present Geometrical Landmark Relations (GLARE), which transform 2D laser scans into pose invariant histogram representations. Potential matches are found in sub-linear time using an efficient Approximate Nearest Neighbour (ANN) search. Experimental results obtained from publicly available datasets demonstrate that GLARE significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings. Our Approach achieves recognition rates of 93% recall at 99% precision for a dataset covering a total path of about 6. 5 km.

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ICRA Conference 2009 Conference Paper

Task-level imitation learning using variance-based movement optimization

Manuel Mühlig
Michael Gienger
Sven Hellbach
Jochen J. Steil
Christian Goerick

Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.

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