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Papers by Felix von Hundelshausen

Abstract. In this paper we present a simple and new algorithm that tracks the contour of several ... more Abstract. In this paper we present a simple and new algorithm that tracks the contour of several homogenous regions in a sequence of images. The method exploits the fact that, when i.e. observing a moving object (exposing a homogenous region), the regions in two consecutive frames often overlap. We show that the method is valuable for the RoboCup domain: It allows to track the green playing field and the goals very efficiently, to detect the white marking lines precisely, enabling us to recognize features in them (the center circle, the quatre circles, corners, the rectangle of the penalty area,...). It is also useful to find the ball and the obstacles. Furthermore, it provides data for path planning based on potential field methods without further computation. We compared the algorithm with the fastest existing method and measured a speed enhancement of 30 percent. In contrast to other methods, our algorithm not only tracks the center of blobs but yields the precise boundary shape ...

it - Information Technology, 2005

SummaryIn this article we describe a self-localization method for soccer robots that solves the “... more SummaryIn this article we describe a self-localization method for soccer robots that solves the “Where am I”-problem by visually perceiving and understanding the structure of the field lines on a soccer playing field. The method accrued from our participation in the RoboCup Middle Size league where fully autonomous robots play soccer on a 8 × 12 m field. The method was successfully applied by our…

Abstract. In this paper we present a simple and new algorithm that tracks the contour of several ... more Abstract. In this paper we present a simple and new algorithm that tracks the contour of several homogenous regions in a sequence of images. The method exploits the fact that, when i.e. observing a moving object (exposing a homogenous region), the regions in two consecutive frames often overlap. We show that the method is valuable for the RoboCup domain: It allows to track the green playing field and the goals very efficiently, to detect the white marking lines precisely, enabling us to recognize features in them (the center circle, the quatre circles, corners, the rectangle of the penalty area,...). It is also useful to find the ball and the obstacles. Furthermore, it provides data for path planning based on potential field methods without further computation. We compared the algorithm with the fastest existing method and measured a speed enhancement of 30 percent. In contrast to other methods, our algorithm not only tracks the center of blobs but yields the precise boundary shape ...

This paper describes the overall system and the principles that yielded the success. The remainde... more This paper describes the overall system and the principles that yielded the success. The remainder of this paper is organized as the following: Section 2 gives an overview of the system. Section 3 describes how visual perception is performed in our implementation. This covers color segmentation, robot self-localization, ball, goal and obstacle detection and tracking. Finally, Section 4 summarizes the results, and describes our future goals

— Humanoid robots, while moving in our everyday environments, necessarily need to recognize objec... more — Humanoid robots, while moving in our everyday environments, necessarily need to recognize objects. Providing robust object definitions for every single object in our environments is challenging and impossible in practice. In this work, we build upon the fact that objects have different uses and humanoid robots, while co-existing with humans, should have the ability of observing humans using the different objects and learn the corresponding object definitions. We present an object recognition algorithm, FOCUS, for Finding Object Classifications through Use and Structure. FOCUS learns structural properties (visual features) of objects by knowing first the object’s affordance properties and observing humans interacting with that object with known activities. FOCUS combines an activity recognizer, flexible and robust to any environment, which captures how an object is used with a low-level visual feature processor. The relevant features are then associated with an object definition wh...

This paper describes an approach for autonomous offroad navigation for the Civilian European Land... more This paper describes an approach for autonomous offroad navigation for the Civilian European Landrobot Trial 2009 (C-ELROB). The main focus in this paper is how to cope with poor GPS conditions such as occurring in forest environments. Therefore the expected and detected problems are stated, and methods how to achieve good autonomous driving performance due to less weigthing of inaccurate GPS information are presented. But first, an introduction how to combine obstacle-avoidance and path following behavior within the reactive so-called tentacles approach is given. Extensions for utilizing visual information for tentacle evaluation as well as the integration of geodetic information to gather information on the road network are presented afterwards. Finally the results gathered from the successful C-ELROB trials are analyzed.

Abstract—While navigating in areas with weak or erroneous GPS signals such as forests or urban ca... more Abstract—While navigating in areas with weak or erroneous GPS signals such as forests or urban canyons, correct map localization is impeded by means of contradicting position hypotheses. Thus, instead of just utilizing GPS positions improved by the robot's ego-...

This paper briefly describes the hardand software system of TAS/UniBw’s entry for the 2009 C-ELRO... more This paper briefly describes the hardand software system of TAS/UniBw’s entry for the 2009 C-ELROB robotic trials, participating at the autonomous navigation scenario. At the core of the system is the so-called tentacles approach to reactive robot navigation, that we already demonstrated successfully at the 2007 C-ELROB trials. Since then, the basic tentacles approach has been extended in many ways. We outline an efficient method for accumulating 3D LIDAR data into multi-layered occupancy grids to be used for tentacle evaluation. We then show how to combine obstacleavoidance and path following behavior within the reactive tentacles approach. Finally, we show how visual information can be used for tentacle evaluation and ERI-Card detection.

Abstract. We describe a new method for detecting features on a marked RoboCup field. We implement... more Abstract. We describe a new method for detecting features on a marked RoboCup field. We implemented the framework for robots with omnidirectional vision, but the method can be easily adapted to other systems. The focus is on the recognition of the center circle and four different corners occurring in the penalty area. Our constructive approach differs from previous methods, in that we aim to detect a whole palette of different features, hierarchically ordered and possibly containing each other. Highlevel features, such as the center circle or the corners, are constructed from low-level features such as arcs and lines. The feature detection process starts with low-level features and iteratively constructs higher features. In RoboCup the method is valuable for robot self-localization; in other fields of application the method is useful for object recognition using shape information. 1

Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.

Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.

2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010

Ki Kunstliche Intelligenz, May 1, 2011

This report gives an overview of the autonomous navigation approach developed for the ground robo... more This report gives an overview of the autonomous navigation approach developed for the ground robot MuCAR-3, partly as a satellite project in the CoTeSys cluster of excellence. Safe robot navigation in general demands that the navigation approach can also cope with situations where GPS data is noisy or even absent and hence great care must be taken when using global

2010 Ieee International Conference on Robotics and Automation, May 3, 2010

In this paper we describe a novel approach to autonomous dirt road following. The algorithm is ab... more In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is

Abstract. In this paper we present a simple and new algorithm that tracks the contour of several ... more Abstract. In this paper we present a simple and new algorithm that tracks the contour of several homogenous regions in a sequence of images. The method exploits the fact that, when i.e. observing a moving object (exposing a homogenous region), the regions in two consecutive frames often overlap. We show that the method is valuable for the RoboCup domain: It allows to track the green playing field and the goals very efficiently, to detect the white marking lines precisely, enabling us to recognize features in them (the center circle, the quatre circles, corners, the rectangle of the penalty area,...). It is also useful to find the ball and the obstacles. Furthermore, it provides data for path planning based on potential field methods without further computation. We compared the algorithm with the fastest existing method and measured a speed enhancement of 30 percent. In contrast to other methods, our algorithm not only tracks the center of blobs but yields the precise boundary shape ...

it - Information Technology, 2005

SummaryIn this article we describe a self-localization method for soccer robots that solves the “... more SummaryIn this article we describe a self-localization method for soccer robots that solves the “Where am I”-problem by visually perceiving and understanding the structure of the field lines on a soccer playing field. The method accrued from our participation in the RoboCup Middle Size league where fully autonomous robots play soccer on a 8 × 12 m field. The method was successfully applied by our…

Abstract. In this paper we present a simple and new algorithm that tracks the contour of several ... more Abstract. In this paper we present a simple and new algorithm that tracks the contour of several homogenous regions in a sequence of images. The method exploits the fact that, when i.e. observing a moving object (exposing a homogenous region), the regions in two consecutive frames often overlap. We show that the method is valuable for the RoboCup domain: It allows to track the green playing field and the goals very efficiently, to detect the white marking lines precisely, enabling us to recognize features in them (the center circle, the quatre circles, corners, the rectangle of the penalty area,...). It is also useful to find the ball and the obstacles. Furthermore, it provides data for path planning based on potential field methods without further computation. We compared the algorithm with the fastest existing method and measured a speed enhancement of 30 percent. In contrast to other methods, our algorithm not only tracks the center of blobs but yields the precise boundary shape ...

This paper describes the overall system and the principles that yielded the success. The remainde... more This paper describes the overall system and the principles that yielded the success. The remainder of this paper is organized as the following: Section 2 gives an overview of the system. Section 3 describes how visual perception is performed in our implementation. This covers color segmentation, robot self-localization, ball, goal and obstacle detection and tracking. Finally, Section 4 summarizes the results, and describes our future goals

— Humanoid robots, while moving in our everyday environments, necessarily need to recognize objec... more — Humanoid robots, while moving in our everyday environments, necessarily need to recognize objects. Providing robust object definitions for every single object in our environments is challenging and impossible in practice. In this work, we build upon the fact that objects have different uses and humanoid robots, while co-existing with humans, should have the ability of observing humans using the different objects and learn the corresponding object definitions. We present an object recognition algorithm, FOCUS, for Finding Object Classifications through Use and Structure. FOCUS learns structural properties (visual features) of objects by knowing first the object’s affordance properties and observing humans interacting with that object with known activities. FOCUS combines an activity recognizer, flexible and robust to any environment, which captures how an object is used with a low-level visual feature processor. The relevant features are then associated with an object definition wh...

This paper describes an approach for autonomous offroad navigation for the Civilian European Land... more This paper describes an approach for autonomous offroad navigation for the Civilian European Landrobot Trial 2009 (C-ELROB). The main focus in this paper is how to cope with poor GPS conditions such as occurring in forest environments. Therefore the expected and detected problems are stated, and methods how to achieve good autonomous driving performance due to less weigthing of inaccurate GPS information are presented. But first, an introduction how to combine obstacle-avoidance and path following behavior within the reactive so-called tentacles approach is given. Extensions for utilizing visual information for tentacle evaluation as well as the integration of geodetic information to gather information on the road network are presented afterwards. Finally the results gathered from the successful C-ELROB trials are analyzed.

Abstract—While navigating in areas with weak or erroneous GPS signals such as forests or urban ca... more Abstract—While navigating in areas with weak or erroneous GPS signals such as forests or urban canyons, correct map localization is impeded by means of contradicting position hypotheses. Thus, instead of just utilizing GPS positions improved by the robot's ego-...

This paper briefly describes the hardand software system of TAS/UniBw’s entry for the 2009 C-ELRO... more This paper briefly describes the hardand software system of TAS/UniBw’s entry for the 2009 C-ELROB robotic trials, participating at the autonomous navigation scenario. At the core of the system is the so-called tentacles approach to reactive robot navigation, that we already demonstrated successfully at the 2007 C-ELROB trials. Since then, the basic tentacles approach has been extended in many ways. We outline an efficient method for accumulating 3D LIDAR data into multi-layered occupancy grids to be used for tentacle evaluation. We then show how to combine obstacleavoidance and path following behavior within the reactive tentacles approach. Finally, we show how visual information can be used for tentacle evaluation and ERI-Card detection.

Abstract. We describe a new method for detecting features on a marked RoboCup field. We implement... more Abstract. We describe a new method for detecting features on a marked RoboCup field. We implemented the framework for robots with omnidirectional vision, but the method can be easily adapted to other systems. The focus is on the recognition of the center circle and four different corners occurring in the penalty area. Our constructive approach differs from previous methods, in that we aim to detect a whole palette of different features, hierarchically ordered and possibly containing each other. Highlevel features, such as the center circle or the corners, are constructed from low-level features such as arcs and lines. The feature detection process starts with low-level features and iteratively constructs higher features. In RoboCup the method is valuable for robot self-localization; in other fields of application the method is useful for object recognition using shape information. 1

Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.

Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.

2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010

Ki Kunstliche Intelligenz, May 1, 2011

This report gives an overview of the autonomous navigation approach developed for the ground robo... more This report gives an overview of the autonomous navigation approach developed for the ground robot MuCAR-3, partly as a satellite project in the CoTeSys cluster of excellence. Safe robot navigation in general demands that the navigation approach can also cope with situations where GPS data is noisy or even absent and hence great care must be taken when using global

2010 Ieee International Conference on Robotics and Automation, May 3, 2010

In this paper we describe a novel approach to autonomous dirt road following. The algorithm is ab... more In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is