Author name cluster

Hartmut Surmann

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.

7 papers

1 author row

IROS Conference 2017 Conference Paper

3D mapping for multi hybrid robot cooperation

Hartmut Surmann
Nils Berninger
Rainer Worst

This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.

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IROS Conference 2006 Conference Paper

3D Cameras for Mobile Robotics

Stefan May
Hartmut Surmann
Maurice Müller
Kai Pervölz

Recently developed 3D time-of-flight cameras have an enormous potential for mobile robotic applications in particular for 3D mapping and 3D navigation tasks. But similar to passive visual sensors, this type of camera is very fragile to changing lighting conditions and environment dynamics, which make it not applicable for mobile robots. The video shows the performance of our solution to environment dynamics. The solution consist of calibration routines, integration time controller and an accuracy filter for the Swiss Ranger SR-2 camera on a mobile robot.

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IROS Conference 2006 Conference Paper

3D time-of-flight cameras for mobile robotics

Stefan May
Bjorn Werner
Hartmut Surmann
Kai Pervölz

Recently developed 3D time-of-flight cameras have an enormous potential for mobile robotic applications in particular for mapping and navigation tasks. This paper presents a new approach for online adaptation of different camera parameters to environment dynamics. These adaptations allow the usage of state-of-the-art 3D cameras reliably in real world environments and enable capturing of 3D scenes with up to 30 frames per second. The performance of the approach is evaluated with respect to different robotic specific tasks in changing environments. A video, showing the performance of the approach, is available at (S. May, et al. , 2006)

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ICRA Conference 2005 Conference Paper

Robust Object Detection at Regions of Interest with an Application in Ball Recognition

Sara Mitri
Simone Frintrop
Kai Pervölz
Hartmut Surmann
Andreas Nüchter

In this paper, we present a new combination of a biologically inspired attention system (VOCUS – Visual Object detection with a CompUtational attention System) with a robust object detection method. As an application, we built a reliable system for ball recognition in the RoboCup context. Firstly, VOCUS finds regions of interest generating a hypothesis for possible locations of the ball. Secondly, a fast classifier verifies the hypothesis by detecting balls at regions of interest. The combination of both approaches makes the system highly robust and eliminates false detections. Furthermore, the system is quickly adaptable to balls in different scenarios: The complex classifier is universally applicable to balls in every context and the attention system improves the performance by learning scenario-specific features quickly from only a few training examples.

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ICRA Conference 2004 Conference Paper

6D SLAM with an Application in Autonomous Mine Mapping

Andreas Nüchter
Hartmut Surmann
Kai Lingemann
Joachim Hertzberg
Sebastian Thrun

To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.

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IROS Conference 2004 Conference Paper

Indoor and outdoor localization for fast mobile robots

Kai Lingemann
Hartmut Surmann
Andreas Nüchter
Joachim Hertzberg

This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computational time, being 33 times faster than real time. The efficiency is achieved by a closed form solution for the matching of two lasers scans, the use of natural landmarks and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.

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IROS Conference 2004 Conference Paper

Saliency-based object recognition in 3D data

Simone Frintrop
Andreas Nüchter
Hartmut Surmann
Joachim Hertzberg

This paper presents a robust and real-time capable recognition system for the fast detection and classification of objects in spatial 3D data. Depth and reflection data from a 3D laser scanner are rendered into images and fed into a saliency-based visual attention system that detects regions of potential interest. Only these regions are examined by a fast classifier. The time saving of classifying objects in salient regions rather than in complete images is linear with the number of trained object classes. Robustness is achieved by the fusion of the bi-modal scanner data; in contrast to camera images, this data is completely illumination independent. The recognition system is trained for two different object classes and evaluated on real indoor data.

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