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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Baum, Marcus Hanebeck, Uwe D. Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (纸本)9781612844558

Fitting conic sections, e.g., ellipses or circles, to noisy data points is a fundamental sensor data processing problem, which frequently arises in robotics. In this paper, we introduce a new procedure for deriving a recursive Gaussian state estimator for fitting conics to data corrupted by additive Gaussian noise. For this purpose, the original exact implicit measurement equation is reformulated with the help of suitable approximations as an explicit measurement equation corrupted by multiplicative noise. Based on stochastic linearization, an efficient Gaussian state estimator is derived for the explicit measurement equation. The performance of the new approach is evaluated by means of a typical ellipse fitting scenario.

关键词： Bayesian methods Current measurement Equations Gaussian noise Mathematical model Noise measurement

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Magnetic Field-based Indoor Localization of a Tracked Robot with Simultaneous Calibration 13

Magnetic Field-based Indoor Localization of a Tracked Robot ...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Siebler, Benjamin Gerstewitz, Tim Sand, Stephan Hanebeck, Uwe D. Institute of Communications and Navigation German Aerospace Center [DLR Germany Germany Intelligent Sensor-Actuator-Systems Laboratory Germany

ISBN: (纸本)9798350320114

Magnetic-field based indoor localization uses distortions of the Earth magnetic field caused by magnetic material in the surrounding of the platform that is to be localized. Existing magnetic localization methods compare measurements from a magnetometer with a magnetic field map to estimate the position of the platform, on which the sensor is mounted. For the comparison it is typically assumed that the magnetometer is calibrated. Unfortunately, for some platforms calibration is not straightforward, particularly when they are large or heavy. In this paper we therefore propose a simultaneous localization and calibration approach. The approach is based on a Rao-Blackwellized particle filter that makes use of the conditional linearity of magnetometer measurements and calibration parameters. The feasibility of the proposed particle filter is evaluated with three data sets recorded with a differential drive robot in an indoor environment. © 2023 IEEE.

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WLAN-Based pedestrian tracking using particle filters and low-cost MEMS sensors

WLAN-Based pedestrian tracking using particle filters and lo...

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4th Workshop on Positioning, Navigation and Communication 2007 (WPNC 07)

作者： Wang, Hui Lenz, Henning Szabo, Andrei Bamberger, Joachim Hanebeck, Uwe D. Siemens AG Corporate Technology Information and Communications CT IC 4 Germany Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe (TH) Germany

ISBN: (纸本)9781424408702

Indoor positioning systems based art Wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.

关键词： indoor positioning pedestrian tracking particle filter MEMS

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Data association in a world model for autonomous systems

Data association in a world model for autonomous systems

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IEEE International Conference on Multisensor Fusion and Integration for intelligent systems

作者： Baum, Marcus Gheţa, Ioana Belkin, Andrey Beyerer, Jiirgen Hanebeck, Uwe D. Institute for Anthropomatics Intelligent Sensor-Actuator-Systems Laboratory Germany Institute for Anthropomatics Visions and Fusion Laboratory Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Karlsruhe Germany

ISBN: (纸本)9781424454266

This contribution introduces a three pillar information storage and management system for modeling the environment of autonomous systems. The main characteristics is the separation of prior knowledge, environment model and sensor information. In the center of the system is the environment model, which provides the autonomous system with information about the current state of the environment. It consists of instances with attributes and relations as virtual substitutes of entities (persons and objects) of the real world. Important features are the representation of uncertain information by means of Degree-of-Belief (DoB) distributions, the information exchange between the three pillars as well as creation, deletion and update of instances, attributes and relations in the environment model. In this work, a Bayesian method for fusing new observations to the environment model is introduced. For this purpose, a Bayesian data association method is derived. The main question answered here is the observation-to-instance mapping and the decision mechanisms for creating a new instance or updating already existing instances in the environment model. © 2010 IEEE.

关键词： Information management

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State Estimation with Event-Based Inputs Using Stochastic Triggers

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IFAC-PapersOnLine 2020年第2期53卷 2324-2329页

作者： Benjamin Noack Christopher Funk Susanne Radtke Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

Event-based communication and state estimation offer the potential to improve resource utilization in networked sensor and control systems significantly. sensor nodes can trigger transmissions when data are deemed useful for the remote estimation units. To improve the estimation performance, the remote estimator can exploit the implicit information conveyed by the event trigger even if no transmission is triggered. The implicit information is typically incorporated into the measurement update of a remote Kalman filter. In this paper, event-triggered transmissions of input data are investigated that enter the prediction step of the remote estimator. By employing a stochastic trigger, the implicit input information remains Gaussian and can easily be incorporated into the remote Kalman filter. The proposed event-based scheme is evaluated in remote tracking scenarios, where system inputs are transmitted aperiodically.

关键词： Kalman Filtering Event-based Estimation Stochastic Triggering Networked Estimation Gaussianity-preserving Triggers

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Event-based LQG Control over Networks with Random Transmission Delays and Packet Losses

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IFAC Proceedings Volumes 2013年第27期46卷 23-30页

作者： Maxim Dolgov, Jörg Fischer Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics Karlsruhe Institute of Technology (KIT) Germany

In Networked Control systems (NCS), data networks not only limit the amount of information exchanged by system components but are also subject to stochastic packet delays and losses. In this paper, we present a controller that simultaneously addresses these problems by combining event-based and sequence-based control methods. At every time step, the proposed controller calculates a sequence of predicted control inputs and based on the expected future LQG costs decides whether it transmits the control sequence to the actuator. The proposed controller is evaluated with simulations.

关键词： Event-based control, sequence-based control, LQG, networked control systems, NCS, stochastic control

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Position and Speed Estimation of PMSMs Using Gaussian Processes

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IFAC-PapersOnLine 2020年第2期53卷 474-481页

作者： Jana Mayer Ajit Basarur Mariana Petrova Fabian Sordon Antonio Zea Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany jana

In this paper, we present a novel low-cost technique to estimate both the position and the speed of a permanent magnet synchronous motor (PMSM) by sensing its stray magnetic field. At an optimal radial and axial distance, a low-cost magnetoresistive sensor is placed outside at the back of the PMSM. The magnetic field values are recorded for one complete rotor revolution at a resolution of less than a degree for different speeds of operation. Gaussian Processes (GPs) are employed to find a mapping function between the magnetic field values of the permanent magnet and the absolute angular positions. Then, by using the learned GP as a measurement function with an Extended Kalman Filter (EKF), both the angular position and speed of a PMSM can be estimated efficiently. Furthermore, we observe that the magnetic field depends not only on the position but also on the angular speed. To address this, we extend the GP to incorporate multivariate inputs. In order to take the periodicity of the data into account, we employ a periodic kernel for the GP. Additionally, a linear basis function model (LBFM) is introduced to incorporate more training points while maintaining the same computational cost. The GP and LBFM approaches are evaluated with data from a real PMSM experiment setup, and the accuracy of the position and speed state estimation is verified against a high-resolution optical encoder used as ground truth.

关键词： Machine learning estimation filtering experiment design magnetic measurements electric motor

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The Spherical Grid Filter for Nonlinear Estimation on the Unit Sphere

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IFAC-PapersOnLine 2020年第2期53卷 2354-2360页

作者： Florian Pfaff Kailai Li Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

Filters for the unit sphere have to consider its inherent periodic nature. Since the unit sphere is a domain of finite size, suitable grids covering the manifold can be provided. We explain the considerations for the grid generation and provide efficient ways to implement the prediction and update steps of a novel grid filter for this manifold. The filter supports nonlinear system and measurement models in the form of transition densities and likelihoods. In the evaluation, the proposed filter achieves a higher estimation accuracy than competing approaches.

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Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds

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IFAC Proceedings Volumes 2008年第2期41卷 9014-9020页

作者： Felix Sawo Frederik Beutler Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe (TH) Germany

This paper addresses the problem of decentralized state estimation of distributed physical phenomena observed by a sensor network. The centralized approaches are not scalable for large sensor networks, because all information has to be transmitted to a powerful central processing node requiring an extensive amount of communication bandwidth and a lot of processing power. Thus, for a decentralized reconstruction of distributed phenomena, we propose a novel methodology consisting of three steps: (a) conversion of the distributed phenomenon into a lumped-parameter system description, (b) decomposition of the resulting system in order to map the description to the actual sensor network, and (c) decomposition of the density representation leading to a decentralized estimation approach. The main problem of a decentralized approach is that due to the propagation of local information through the network, unknown correlations are caused. This fact needs to be considered during the reconstruction process in order to get correct and consistent estimation results. For that reason, we employ a robust estimator (based on Covariance Bounds ) for the local reconstruction update on each sensor node. By this means, the individual sensor nodes are able to estimate the local state of the distributed phenomenon using local estimates obtained and communicated by adjacent nodes only. The information about their correlations is not stored in the sensor network.

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Three pillar information management system for modeling the environment of autonomous systems

Three pillar information management system for modeling the ...

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2010 8th IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement systems, VECIMS 2010

作者： Gheţa, Ioana Baum, Marcus Belkin, Andrey Beyerer, Jürgen Hanebeck, Uwe D. Institute for Anthropomatics Vision and Fusion Laboratory Karlsruhe Germany Institute for Anthropomatics Intelligent Sensor-Actuator-Systems Laboratory Karlsruhe Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Karlsruhe Germany

ISBN: (纸本)9781424459063

This contribution is about an information management and storage system for modeling the environment of autonomous systems. The three pillars of the system consist of prior knowledge, environment model and sensory information. The main pillar is the environment model, which supplies the autonomous system with relevant information about its current environment. For this purpose, an abstract representation of the real world is created, where instances with attributes and relations serve as virtual substitutes of entities (persons and objects) of the real world. The environment model is created based on sensory information about the real world. The gathered sensory information is typically uncertain in a stochastic sense and is represented in the environment model by means of Degree-of- Belief (DoB) distributions. The prior knowledge contains all relevant background knowledge (e. g., concepts organized in ontologies) for creating and maintaining the environment model. The concept of the three pillar information system has previously been published. Therefore this contribution focuses on further central properties of the system. Furthermore, the development status and possible applications as well as evaluation scenarios are discussed. © 2010 IEEE.

关键词： Stochastic systems

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