RoboLog Koblenz is participating in the 2D Simulated Soccer com-petition for the 6th time. In thi... more RoboLog Koblenz is participating in the 2D Simulated Soccer com-petition for the 6th time. In this paper we focus on two of the features we im-proved from last year to this year. To improve the goal shot behavior of the RoboLog agents we included a scoring policy learned from observing games from RoboCup03 and the SSIL. In addition a smart dribbling algorithm based on In-verse Steering Behaviors–an extension to steering behaviors–has been added to the agents.
ABSTRACT Lane detection and tracking is still a challenging task. Here, we combine the recently i... more ABSTRACT Lane detection and tracking is still a challenging task. Here, we combine the recently introduced Statistical Hough transform (SHT) with a Particle Filter (PF) and show its application for robust lane tracking. SHT improves the standard Hough transform (HT) which was shown to work well for lane detection. We use the local descriptors of the SHT as measurement for the PF, and show how a new three kernel density based observation model can be modeled based on the SHT and used with the PF. The application of the former becomes feasible by the reduced computations achieved with the tracking algorithm. We demonstrate the use of the resulting algorithm for lane detection and tracking by applying it to images freed from the perspective effect achieved by applying Inverse Perspective Mapping (IPM). The presented results show the robustness of the presented algorithm.
2011 IEEE International Conference on Robotics and Automation, 2011
ABSTRACT This paper presents a probabilistic algorithm for lane shape estimation in an urban envi... more ABSTRACT This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the so-called Partitioned Particle filter, an improvement of the traditional Particle filter, and the linear-parabolic lane model which alleviates many shortcomings of traditional lane models. The former improves the traditional Particle filter by subdividing the whole state space of particles into several subspaces and estimating those subspaces in a hierarchical structure, such that the number of particles for each subspace is flexible and the robustness of the whole system is increased. Furthermore, we introduce a new statistical observation model, an important part of the Particle filter, where we use multi kernel density to model the probability distribution of lane parameters. Our observation model considers not only color and position information as image cues, but also the image gradient. Our experimental results illustrate the robustness and efficiency of our algorithm even when confronted with challenging scenes.
2011 IEEE Statistical Signal Processing Workshop (SSP), 2011
In this contribution, we introduce a new state estimation filter for nonlinear estimation and sen... more In this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call central difference information filter (CDIF). As we know, the extended information filter (EIF) has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcomings can be compensated by utilizing sigma point information filters (SPIFs), e.g., the unscented information filter (UIF), which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirling's interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our algorithm, a classic space vehicle reentry tracking simulation is used.
2011 11th IEEE-RAS International Conference on Humanoid Robots, 2011
Generalizing objects in an action-context by a robot, for example addressing the problem: "Which ... more Generalizing objects in an action-context by a robot, for example addressing the problem: "Which items can be cut with which tools?", is an unresolved and difficult problem. Answering such a question defines a complete action class and robots cannot do this so far. We use a bootstrapping mechanism similar to that known from human language acquisition, and combine language-with image-analysis to create action classes built around the verb (action) in an utterance. A human teaches the robot a certain sentence, for example: "Cut a sausage with a knife", from where on the machine generalizes the arguments (nouns) that the verb takes and searches for possible alternative nouns. Then, by ways of an internet-based image search and a classification algorithm, image classes for the alternative nouns are extracted, by which a large "picture book" of the possible objects involved in an action is created. This concludes the generalization step. Using the same classifier, the machine can now also perform a recognition procedure. Without having seen the objects before, it can analyze a visual scene, discovering, for example, a cucumber and a mandolin, which match to the earlier found nouns allowing it to suggest actions like: "I could cut a cucumber with a mandolin". The algorithm for generalizing objects by analyzing language (GOAL) presented here, allows, thus, generalization and recognition of objects in an action-context. It can then be combined with methods for action execution (e.g. action generation-based on human demonstration) to execute so far unknown actions.
Advances in Climbing and Walking Robots - Proceedings of 10th International Conference (CLAWAR 2007), 2007
This paper presents a modular neural network controller for fast locomotion of a quadruped robot.... more This paper presents a modular neural network controller for fast locomotion of a quadruped robot. It was generated by artificial evolution techniques using a physical simulation of the Sony Aibo ERS-7. Co-evolution was used to develop neuromodules controlling the single legs as well as the coordination between the four legs. The final neurocontroller utilizes a central pattern generator and does not make use of available sensory inputs. In experiments with the physical robot a top walking speed of 47.34 cm/s was measured, where lateral leg movement contributed considerably to the achieved high velocity.
If we stored every bit of input, the storage capacity of our nervous system would be reached afte... more If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis. Keywords Working memory • Short-term memory • Long-term memory • Short-term plasticity • Long-term plasticity • Structural plasticity • Time scales This article forms part of a special issue of Biological Cybernetics entitled "Multimodal and Sensorimotor Bionics". Christian Tetzlaff and Christoph Kolodziejski contributed equally for this study.
IEEE Transactions on Intelligent Transportation Systems, 2000
ABSTRACT In this paper, we present a novel measurement model for particle-filter-based lane shape... more ABSTRACT In this paper, we present a novel measurement model for particle-filter-based lane shape estimation. Recently, the particle filter has been widely used to solve lane detection and tracking problems, due to its simplicity, robustness, and efficiency. The key part of the particle filter is the measurement model, which describes how well a generated hypothesis (a particle) fits current visual cues in the image. Previous methods often simply combine multiple visual cues in a likelihood function without considering the uncertainties of local visual cues and the accurate probability relationship between visual cues and the lane model. In contrast, this paper derives a new measurement model by utilizing multiple kernel density to precisely estimate this probability relationship. The uncertainties of local visual cues are considered and modeled by Gaussian kernels. Specifically, we use a linear-parabolic model to describe the shape of lane boundaries on a top-view image and a partitioned particle filter (PPF), integrating it with our novel measurement model to estimate lane shapes in consecutive frames. Finally, the robustness of the proposed algorithm with the new measurement model is demonstrated on the DRIVSCO data sets.
IEEE Transactions on Intelligent Transportation Systems, 2000
To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consid... more To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consider individual driving styles. Here, we present a system that learns a human's basic driving behavior and demonstrate its use as ADAS by issuing alerts when detecting inconsistent driving behavior. In contrast to much other work in this area, which is based on or obtained from simulation, our system is implemented as a multithreaded parallel central processing unit (CPU)/graphics processing unit (GPU) architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. It also implements a method for detecting independently moving objects (IMOs) for spotting obstacles. Both learning and IMO detection algorithms are data driven and thus improve above the limitations of model-based approaches. The system's ability to imitate the teacher's behavior is analyzed on known and unknown streets, and results suggest its use for steering assistance but limit the use of the acceleration signal to curve negotiation. We propose that this ability to adapt to the driver can lead to better acceptance of ADAS, which is an important sales argument. Index Terms-Advanced individualized driver-assistance system, driving, imitation learning, independently moving object (IMO), real-time system. A DVANCED driver-assistance systems (ADASs) that adapt to the individual driver have high potential in the car industry since they can reduce the risk of accidents while providing a high degree of comfort. Conventional systems are based on a general moment-to-moment assessment of road and driving parameters. To arrive at a judgment of the current Manuscript
ABSTRACT The sigma-point information filters employ a number of deterministic sigma-points to cal... more ABSTRACT The sigma-point information filters employ a number of deterministic sigma-points to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigma-points can be generated by the unscented transform or Stirling's interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the square-root extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the square-root unscented information filter (SRUIF) might lose the positive-definiteness due to the negative Cholesky update, whereas the square-root central difference information filter (SRCDIF) has only positive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.
If we stored every bit of input, the storage capacity of our nervous system would be reached afte... more If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis. Keywords Working memory • Short-term memory • Long-term memory • Short-term plasticity • Long-term plasticity • Structural plasticity • Time scales This article forms part of a special issue of Biological Cybernetics entitled "Multimodal and Sensorimotor Bionics". Christian Tetzlaff and Christoph Kolodziejski contributed equally for this study.
RoboLog Koblenz is participating in the 2D Simulated Soccer com-petition for the 6th time. In thi... more RoboLog Koblenz is participating in the 2D Simulated Soccer com-petition for the 6th time. In this paper we focus on two of the features we im-proved from last year to this year. To improve the goal shot behavior of the RoboLog agents we included a scoring policy learned from observing games from RoboCup03 and the SSIL. In addition a smart dribbling algorithm based on In-verse Steering Behaviors–an extension to steering behaviors–has been added to the agents.
ABSTRACT Lane detection and tracking is still a challenging task. Here, we combine the recently i... more ABSTRACT Lane detection and tracking is still a challenging task. Here, we combine the recently introduced Statistical Hough transform (SHT) with a Particle Filter (PF) and show its application for robust lane tracking. SHT improves the standard Hough transform (HT) which was shown to work well for lane detection. We use the local descriptors of the SHT as measurement for the PF, and show how a new three kernel density based observation model can be modeled based on the SHT and used with the PF. The application of the former becomes feasible by the reduced computations achieved with the tracking algorithm. We demonstrate the use of the resulting algorithm for lane detection and tracking by applying it to images freed from the perspective effect achieved by applying Inverse Perspective Mapping (IPM). The presented results show the robustness of the presented algorithm.
2011 IEEE International Conference on Robotics and Automation, 2011
ABSTRACT This paper presents a probabilistic algorithm for lane shape estimation in an urban envi... more ABSTRACT This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the so-called Partitioned Particle filter, an improvement of the traditional Particle filter, and the linear-parabolic lane model which alleviates many shortcomings of traditional lane models. The former improves the traditional Particle filter by subdividing the whole state space of particles into several subspaces and estimating those subspaces in a hierarchical structure, such that the number of particles for each subspace is flexible and the robustness of the whole system is increased. Furthermore, we introduce a new statistical observation model, an important part of the Particle filter, where we use multi kernel density to model the probability distribution of lane parameters. Our observation model considers not only color and position information as image cues, but also the image gradient. Our experimental results illustrate the robustness and efficiency of our algorithm even when confronted with challenging scenes.
2011 IEEE Statistical Signal Processing Workshop (SSP), 2011
In this contribution, we introduce a new state estimation filter for nonlinear estimation and sen... more In this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call central difference information filter (CDIF). As we know, the extended information filter (EIF) has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcomings can be compensated by utilizing sigma point information filters (SPIFs), e.g., the unscented information filter (UIF), which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirling's interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our algorithm, a classic space vehicle reentry tracking simulation is used.
2011 11th IEEE-RAS International Conference on Humanoid Robots, 2011
Generalizing objects in an action-context by a robot, for example addressing the problem: "Which ... more Generalizing objects in an action-context by a robot, for example addressing the problem: "Which items can be cut with which tools?", is an unresolved and difficult problem. Answering such a question defines a complete action class and robots cannot do this so far. We use a bootstrapping mechanism similar to that known from human language acquisition, and combine language-with image-analysis to create action classes built around the verb (action) in an utterance. A human teaches the robot a certain sentence, for example: "Cut a sausage with a knife", from where on the machine generalizes the arguments (nouns) that the verb takes and searches for possible alternative nouns. Then, by ways of an internet-based image search and a classification algorithm, image classes for the alternative nouns are extracted, by which a large "picture book" of the possible objects involved in an action is created. This concludes the generalization step. Using the same classifier, the machine can now also perform a recognition procedure. Without having seen the objects before, it can analyze a visual scene, discovering, for example, a cucumber and a mandolin, which match to the earlier found nouns allowing it to suggest actions like: "I could cut a cucumber with a mandolin". The algorithm for generalizing objects by analyzing language (GOAL) presented here, allows, thus, generalization and recognition of objects in an action-context. It can then be combined with methods for action execution (e.g. action generation-based on human demonstration) to execute so far unknown actions.
Advances in Climbing and Walking Robots - Proceedings of 10th International Conference (CLAWAR 2007), 2007
This paper presents a modular neural network controller for fast locomotion of a quadruped robot.... more This paper presents a modular neural network controller for fast locomotion of a quadruped robot. It was generated by artificial evolution techniques using a physical simulation of the Sony Aibo ERS-7. Co-evolution was used to develop neuromodules controlling the single legs as well as the coordination between the four legs. The final neurocontroller utilizes a central pattern generator and does not make use of available sensory inputs. In experiments with the physical robot a top walking speed of 47.34 cm/s was measured, where lateral leg movement contributed considerably to the achieved high velocity.
If we stored every bit of input, the storage capacity of our nervous system would be reached afte... more If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis. Keywords Working memory • Short-term memory • Long-term memory • Short-term plasticity • Long-term plasticity • Structural plasticity • Time scales This article forms part of a special issue of Biological Cybernetics entitled "Multimodal and Sensorimotor Bionics". Christian Tetzlaff and Christoph Kolodziejski contributed equally for this study.
IEEE Transactions on Intelligent Transportation Systems, 2000
ABSTRACT In this paper, we present a novel measurement model for particle-filter-based lane shape... more ABSTRACT In this paper, we present a novel measurement model for particle-filter-based lane shape estimation. Recently, the particle filter has been widely used to solve lane detection and tracking problems, due to its simplicity, robustness, and efficiency. The key part of the particle filter is the measurement model, which describes how well a generated hypothesis (a particle) fits current visual cues in the image. Previous methods often simply combine multiple visual cues in a likelihood function without considering the uncertainties of local visual cues and the accurate probability relationship between visual cues and the lane model. In contrast, this paper derives a new measurement model by utilizing multiple kernel density to precisely estimate this probability relationship. The uncertainties of local visual cues are considered and modeled by Gaussian kernels. Specifically, we use a linear-parabolic model to describe the shape of lane boundaries on a top-view image and a partitioned particle filter (PPF), integrating it with our novel measurement model to estimate lane shapes in consecutive frames. Finally, the robustness of the proposed algorithm with the new measurement model is demonstrated on the DRIVSCO data sets.
IEEE Transactions on Intelligent Transportation Systems, 2000
To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consid... more To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consider individual driving styles. Here, we present a system that learns a human's basic driving behavior and demonstrate its use as ADAS by issuing alerts when detecting inconsistent driving behavior. In contrast to much other work in this area, which is based on or obtained from simulation, our system is implemented as a multithreaded parallel central processing unit (CPU)/graphics processing unit (GPU) architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. It also implements a method for detecting independently moving objects (IMOs) for spotting obstacles. Both learning and IMO detection algorithms are data driven and thus improve above the limitations of model-based approaches. The system's ability to imitate the teacher's behavior is analyzed on known and unknown streets, and results suggest its use for steering assistance but limit the use of the acceleration signal to curve negotiation. We propose that this ability to adapt to the driver can lead to better acceptance of ADAS, which is an important sales argument. Index Terms-Advanced individualized driver-assistance system, driving, imitation learning, independently moving object (IMO), real-time system. A DVANCED driver-assistance systems (ADASs) that adapt to the individual driver have high potential in the car industry since they can reduce the risk of accidents while providing a high degree of comfort. Conventional systems are based on a general moment-to-moment assessment of road and driving parameters. To arrive at a judgment of the current Manuscript
ABSTRACT The sigma-point information filters employ a number of deterministic sigma-points to cal... more ABSTRACT The sigma-point information filters employ a number of deterministic sigma-points to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigma-points can be generated by the unscented transform or Stirling's interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the square-root extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the square-root unscented information filter (SRUIF) might lose the positive-definiteness due to the negative Cholesky update, whereas the square-root central difference information filter (SRCDIF) has only positive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.
If we stored every bit of input, the storage capacity of our nervous system would be reached afte... more If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis. Keywords Working memory • Short-term memory • Long-term memory • Short-term plasticity • Long-term plasticity • Structural plasticity • Time scales This article forms part of a special issue of Biological Cybernetics entitled "Multimodal and Sensorimotor Bionics". Christian Tetzlaff and Christoph Kolodziejski contributed equally for this study.
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