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Christian Dornhege – Publikationen

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2011

• Christian Dornhege und Alexander Kleiner.
  A Frontier-Void-Based Approach for Autonomous Exploration in 3D.
  In Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). 2011.
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  We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.

• Matthias Westphal, Christian Dornhege, Stefan Wölfl, Marc Gissler und Bernhard Nebel.
  Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning.
  Spatial Cognition & Computation: An Interdisciplinary Journal 11 (1), S. 75-102. 2011.
  (BIB)
  

• R. Kümmerle, B. Steder, Christian Dornhege, Alexander Kleiner, G. Grisetti und W. Burgard.
  Large Scale Graph-based SLAM using Aerial Images as Prior Information.
  Autonomous Robots 30, S. 25-39. 2011.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

2010

• Kai M. Wurm, Christian Dornhege, Patrick Eyerich, Cyrill Stachniss, Bernhard Nebel und Wolfram Burgard.
  Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning.
  In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). 2010.
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  The problem of autonomously exploring an environment with a team of robots received considerable attention in the past. However, there are relatively few approaches to coordinate teams of robots that are able to deploy and retrieve other robots. Efficiently coordinating the exploration with such marsupial robots requires advanced planning mechanisms that are able to consider symbolic deployment and retrieval actions. In this paper, we propose a novel approach for coordinating the exploration with marsupial robot teams. Our method integrates a temporal symbolic planner that explicitly considers deployment and retrieval actions with a traditional cost-based assignment procedure. Our approach has been implemented and evaluated in several simulated environments and with varying team sizes. The results demonstrate that our proposed method is able to coordinate marsupial teams of robots to efficiently explore unknown environments.

• Alexander Kleiner und Christian Dornhege.
  Mapping for the Support of First Responders in Critical Domains.
  Journal of Intelligent and Robotic Systems (JINT), S. 1-29. 2010.
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  In critical domains such as urban search and rescue (USAR), and bomb disposal, the deployment of teleoperated robots is essential to reduce the risk of first responder personnel. Teleoperation is a difficult task, particularly when controlling robots from an isolated safety zone. In general, the operator has to solve simultaneously the problems of mission planning, target identification, robot navigation, and robot control. We introduce a system to support teleoperated navigation with real-time mapping consisting of a two-step scan matching method that re-considers data associations during the search. The algorithm processes data from laser range finder and gyroscope only, thereby it is independent from the robot platform. Furthermore, we introduce a user-guided procedure for improving the global consistency of maps generated by the scan matcher. Globally consistent maps are computed by a graph-based maximum likelihood method that is biased by localizing crucial parts of the scan matcher trajectory on a prior given geo-tiff image. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system was evaluated in a test maze by first responders during the Disaster City event in Texas 2008.

2009

• Marc Gissler, Christian Dornhege, Bernhard Nebel und Matthias Teschner.
  Deformable Proximity Queries and their Application in Mobile Manipulation Planning.
  In Symposium on Visual Computing (ISVC 2009), S. 79-88. AAAI Press 2009.
  (Abstract einblenden) (Abstract ausblenden) (BIB)
  

• Alexander Kleiner und Christian Dornhege.
  Operator-Assistive Mapping in Harsh Environments.
  In IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2009), S. 1-6. 2009.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  Teleoperation is a difficult task, particularly when controlling robots from an isolated operator station. In general, the operator has to solve nearly blindly the problems of mission planning, target identification, robot navigation, and robot control at the same time. The goal of the proposed system is to support teleoperated navigation with real-time mapping. We present a novel scan matching technique that re-considers data associations during the search, enabling robust pose estimation even under varying roll and pitch angle of the robot enabling mapping on rough terrain. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system has been evaluated in a test maze by first responders during the Disaster City event in Texas 2008. Finally, experiments conducted within different environments show that the system yields comparably accurate maps in real-time when compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.

• Christian Dornhege, Marc Gissler, Matthias Teschner und Bernhard Nebel.
  Integrating Symbolic and Geometric Planning for Mobile Manipulation.
  In IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2009). 2009.
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  Mobile manipulation requires to solve multiple subproblems. One is planning in high-dimensional configuration spaces, that we approach in this work. We decompose the manipulation problem into a symbolic and a geometric part. The symbolic part is implemented as a classical symbolic planner that tightly integrates a geometric planner enabling us to efficiently generate correct plans. A probabilistic roadmap planner constitutes the geometric part. During the computation of the roadmap we utilize proximity queries to determine non-colliding configurations and to verify collision-free paths between configurations accurately and efficiently. We demonstrate experiments in two scenarios, one of these being the manipulator dexterity test scenario that was used in NIST's response robot evaluation in Disaster City.

• Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner und Bernhard Nebel.
  Semantic Attachments for Domain-Independent Planning Systems.
  In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), S. 114-121. AAAI Press 2009.
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  Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

• Moritz Göbelbecker und Christian Dornhege.
  Realistic Cities in Simulated Environments - An Open Street Map to Robocup Rescue Converter.
  In Online-Proceedings of the Fourth International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disaster (SRMED 2009). 2009.
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  A general problem when developing large scale disaster simulation environments is to acquire GIS data. In this work, we tackle the problem of map generation from public sources. Usually the major problem is not only the data conversion itself, but to get access to the data at all. We solve this problem by using the website OpenStreetMap.org, that provides mapping data for the whole world in a wiki-style concept, as our source of data, thus being able to generate maps for almost any city. The data is converted to the format required by the Robocup Rescue Simulation System, enabling simulations on various real-world scenarios.

• Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss und Alexander Kleiner.
  On Measuring the Accuracy of SLAM Algorithms.
  Autonomous Robots 27 (4), S. 387-407. 2009.
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  In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.

  We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

• Wolfram Burgard, Cyrill Stachniss, Giorgio Grisetti, Bastian Steder, Rainer Kümmerle, Christian Dornhege, Michael Ruhnke, Alexander Kleiner und Juan D. Tardos.
  A Comparison of SLAM Algorithms Based on a Graph of Relations.
  In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), S. 2089-2095. IEEE 2009.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches in the sense that it considers the energy needed to deform the trajectory estimated by a SLAM approach to the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations. We believe that our benchmarking framework allows the user an easy analysis and objective comparisons between different SLAM approaches.

• Rainer Kümmerle, Bastian Steder, Christian Dornhege, Alexander Kleiner, Giorgio Grisetti und Wolfram Burgard.
  Large Scale Graph-based SLAM using Aerial Images as Prior Information.
  In Proceedings of 2009 Robotics: Science and Systems (RSS 2009). 2009.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

2007

• Christian Dornhege und Alexander Kleiner.
  Fully autonomous planning and obstacle negotiation on rough terrain using behavior maps.
  In Video Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007). San Diego, California 2007.
  

• Christian Dornhege und Alexander Kleiner.
  Behavior maps for online planning of obstacle negotiation and climbing on rough terrain.
  In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), S. 3005-3011. 2007.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

• Alexander Kleiner und Christian Dornhege.
  Real-time Localization and Elevation Mapping within Urban Search and Rescue Scenarios.
  Journal of Field Robotics 24, S. 723-745. 2007.
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  Urban Search And Rescue (USAR) is a time critical task. Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. One goal in Rescue Robotics is to have a team of heterogeneous robots that explore autonomously, or partially guided by an incident commander, the disaster area. Their task is to jointly create a map of the terrain and to register victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of Simultaneous Localization and Mapping (SLAM), consisting of a continuous state estimation problem and a discrete data association problem. Extraordinary circumstances after a real disaster make it very hard to apply common techniques. Many of these have been developed under strong assumptions, for example, they require polygonal structures, such as typically found in office-like environments. Furthermore, most techniques are not deployable in real-time. In this paper we propose real-time solutions for localization and mapping, which all have been extensively evaluated within the test arenas of the National Institute of Standards and Technology (NIST). We specifically deal with the problems of vision-based pose tracking on tracked vehicles, the building of globally consistent maps based on a network of RFID tags, and the building of elevation maps from readings of a tilted Laser Range Finder (LRF). Our results show that these methods lead under modest computational requirements to good results within the utilized testing arenas.

• Alexander Kleiner, Christian Dornhege und Dali Sun.
  Mapping disaster areas jointly: RFID -Coordinated SLAM by Humans and Robots.
  In Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2007), S. 1-6. 2007.
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  We consider the problem of jointly performing SLAM by humans and robots in Urban Search And Rescue (USAR) scenarios. In this context, SLAM is a challenging task. First, places are hardly re-observable by vision techniques since visibility might be affected by smoke and fire. Second, loop-closure is cumbersome due to the fact that firemen will intentionally try to avoid performing loops when facing the reality of emergency response, e.g.USAR, while they are searching for victims. Furthermore, there might be places that are only accessible to robots, making it necessary to integrate humans and robots into one team for mapping the area after a disaster. In this paper, we introduce a method for jointly correcting individual trajectories of humans and robots by utilizing RFID technology for data association. Hereby the poses of humans and robots are tracked by a PDR (Pedestrian Dead Reckoning), and slippage sensitive odometry, respectively. We conducted extensive experiments with a team of humans, and a human-robot team within a semi-outdoor environment. Results from these experiments show that the introduced method allows to improve single trajectories based on the joint graph, even if they do not contain any loop.

2006

• Alexander Kleiner, Christian Dornhege, Rainer Kuemmerle, Michael Ruhnke, Bastian Steder, Bernhard Nebel, Patrick Doherty, Mariusz Wzorek, Piotr Rudol, Gianpaolo Conte, S. Durante und D. Lundstrom.
  RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
  In CDROM Proceedings of the International RoboCup Symposium '05. Bremen, Germany 2006.
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  This paper describes the approach of the RescueRobots Freiburg team, which is a team of students from the University of Freiburg that originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team (RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial vehicle platform. Our approach covers RFID-based SLAM and exploration, autonomous detection of relevant 3D structures, visual odometry, and autonomous victim identification. Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism for the active distribution of RFID tags.

• Christian Dornhege und Alexander Kleiner.
  Visual Odometry for Tracked Vehicles.
  In Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2006). 2006.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot's motion. Each image vector is taken for a voting that best explains the robot's motion. Image vectors vote according to a previously trained ^\m2102tile coding^\m2112 classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.

2005

• Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler und Bernhard Nebel.
  Successful Search and Rescue in Simulated Disaster Areas.
  In Proceedings of the International RoboCup Symposium '05. Osaka, Japan 2005.
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  RoboCupRescue Simulation is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. This paper presents the comprehensive search and rescue approach of the ResQ Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup 2004. Specific contributions include the predictions of travel costs and civilian lifetime, the efficient coordination of an active disaster space exploration, as well as an any-time rescue sequence optimization based on a genetic algorithm. We compare the performances of our team and others in terms of their capability of extinguishing fires, freeing roads from debris, disaster space exploration, and civilian rescue. The evaluation is carried out with information extracted from simulation log files gathered during RoboCup 2004. Our results clearly explain the success of our team, and also confirm the scientific approaches proposed in this paper.

• Alexander Kleiner, Bastian Steder, Christian Dornhege, Daniel Hoefler, Daniel Meyer-Delius, Johann Prediger, Joerg Stueckler, Kolja Glogowski, Markus Thurner, Matthias Luber, Michael Schnell, Rainer Kuemmerle, Timothy Burk, Tobias Braeuer und Bernhard Nebel.
  RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
  In CDROM Proceedings of the International RoboCup Symposium '05. Osaka, Japan 2005.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  This paper describes the approach of the RescueRobots Freiburg team. RescueRobots Freiburg is a team of students from the university of Freiburg, that originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team (RoboCupRescue Simulation). Due to the high versatility of the RoboCupRescue competition we tackle the three arenas by a a twofold approach: On the one hand we want to introduce robust vehicles that can safely be teleoperated through rubble and building debris while constructing three-dimensional maps of the environment. On the other hand we want to introduce a team of autonomous robots that quickly explore a large terrain while building a two-dimensional map. This two solutions are particularly wellsuited for the red and yellow arena, respectively. Our solution for the orange arena will finally be decided between these two, depending on the capabilities of both approaches at the venue. In this paper, we introduce some preliminary results that we achieved so far from map building, localization, and autonomous victim identification. Furthermore we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism for the active distribution of RFID tags. 1 Introduction RescueRobots Freiburg is a team of students from the university of Freiburg. The team originates from the former CS Freiburg team[6], which won three times the RoboCup world championship in the RoboCupSoccer F2000 league, and the ResQ Freiburg team[2], which won the last RoboCup world championship in the RoboCupRescue Simulation league. The team approach proposed in this paper is based on experiences gathered at RoboCup during the last six years. Due to the high versatility of the RoboCupRescue competition we tackle the three arenas by a twofold approach: On the one hand we want to introduce a vehicle that can safely be teleoperated through rubble and building debris while constructing threedimensional maps of the environment. On the other hand we want to introduce an autonomous team of robots that quickly explore a large terrain while building a twodimensional map. This two solutions are particularly well-suited for the red and yellow arena, respectively. Our solution for the orange arena will finally be decided between these two, depending on the capabilities of both approaches at the venue.

2004

• Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger und Joerg Stueckler.
  ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
  In CDROM Proceedings of the International RoboCup Symposium '04. Lisbon, Portugal 2004.
  (PDF)
  

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