Download or read book Using Inertial Sensors for Position and Orientation Estimation written by Manon Kok and published by . This book was released on 2017 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors.We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.
Download or read book Using Inertial Sensors for Position and Orientation Estimation written by Manon Kok and published by . This book was released on 2018-01-31 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microelectromechanical system (MEMS) inertial sensors have become ubiquitous in modern society. Built into mobile telephones, gaming consoles, virtual reality headsets, we use such sensors on a daily basis. They also have applications in medical therapy devices, motion-capture filming, traffic monitoring systems, and drones. While providing accurate measurements over short time scales, this diminishes over longer periods. To date, this problem has been resolved by combining them with additional sensors and models. This adds both expense and size to the devices. This tutorial focuses on the signal processing aspects of position and orientation estimation using inertial sensors. It discusses different modelling choices and a selected number of important algorithms that engineers can use to select the best options for their designs. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. Engineers, researchers, and students deploying MEMS inertial sensors will find that this tutorial is an essential monograph on how to optimize their designs.
Download or read book Probabilistic modeling for sensor fusion with inertial measurements written by Manon Kok and published by Linköping University Electronic Press. This book was released on 2016-12-15 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets. The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account. The first contribution of this thesis is a tutorial paper that describes the signal processing foundations underlying position and orientation estimation using inertial sensors. In a second contribution, we use data from multiple inertial sensors placed on the human body to estimate the body's pose. A biomechanical model encodes the knowledge about how the different body segments are connected to each other. We also show how the structure inherent to this problem can be exploited. This opens up for processing long data sets and for solving the problem in a distributed manner. Inertial sensors can also be combined with time of arrival measurements from an ultrawideband (UWB) system. We focus both on calibration of the UWB setup and on sensor fusion of the inertial and UWB measurements. The UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution. This distribution naturally handles the possibility of measurement delays due to multipath and non-line-of-sight conditions while not allowing for the possibility of measurements arriving early, i.e. traveling faster than the speed of light. Finally, inertial sensors can be combined with magnetometers. We derive an algorithm that can calibrate a magnetometer for the presence of metallic objects attached to the sensor. Furthermore, the presence of metallic objects in the environment can be exploited by using them as a source of position information. We present a method to build maps of the indoor magnetic field and experimentally show that if a map of the magnetic field is available, accurate position estimates can be obtained by combining inertial and magnetometer measurements.
Download or read book Short Term Tracking of Orientation with Inertial Sensors written by and published by . This book was released on 2018 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past several years, IMU's have been widely used to measure the orientation of a moving body over a continuous period of time. Although, inertial navigation is a common approach for estimating the orientation, it greatly suffers from the accumulation of error in the orientation estimation. Most of the current common practices apply zero velocity update as a calibration method to address this problem and improve the estimation accuracy. However, this approach requires the sensors to be stationary frequently. This thesis introduces a novel method of calibration for estimating the elevation and bank angles of the orientation over a persistent human movement utilizing accelerometers and gyroscopes. The proposed technique incorporates the prior knowledge about the human motion to the estimation of the orientation to prevent the estimated position from growing unboundedly. The measurement model is designed to estimate the position for T seconds in the future. The knowledge of the estimated position for few seconds further in the future provides a feedback for orientation estimation during the periods of time when the accelerometer's readings are significantly deviated from gravity. This work evaluates the performance of the proposed method in two different ways: 1. a model of human movement is designed to generate synthetic data which resembles human motion. 2. an experimental design is implemented using a robot arm and an actual IMU to capture real data. The performance of the new technique is compared with the results from the inertial navigation approach. It is demonstrated that the new method significantly improves the accuracy of the orientation estimation.
Download or read book State Estimation Solely Based on Prior Knowledge and Inertial Sensors written by Tom Lucas Koller and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: How do we localize ourselves? Ever since GPS exists, it is common to know where we are and how to get to our desired location. Unfortunately, GPS is unavailable indoors. Scientists are looking for an alternative technology that can fill this localization gap. One approach is to fuse knowledge about our environment and our movement measured with inertial sensors. A particular difficulty of these sensors is that their pose (position and orientation) estimation error grows over time. This so-called drift can lead to false estimations such as passing through a wall. These false estimations could be corrected by using prior knowledge of the wall's location. In this work, I investigate how prior knowledge can be fused with inertial sensor measurements. The practical aim of this thesis is to eliminate the drift without additional sensors. I investigate three types of prior knowledge regarding the environment and the movement: The human gait pattern, terrain maps, and event-domain maps. For all three types, I follow the concept of modeling the prior knowledge as probability distributions of the system's state. This modeling enables the usage of standard probability-based algorithms to estimate the position and orientation and to fuse the knowledge with sensor measurements. The human gait is an alternating pattern of stance and swing phases. I show a new approach based on the Interacting Multiple Model Filter that can detect the phase and improve the velocity estimate of the inertial sensor. The approach automatically detects whether the sensor measurements match the probability distribution of the stance or swing phase. Simultaneously, it corrects the measurement errors of the inertial sensor by taking into account the probability distributions. The evaluation shows the potential of this method, albeit further development is required to outperform state of the art approaches. Terrain maps define the height of a vehicle or a human given its position in the horizontal plane. This can be modeled as a so-called pseudo measurement. We act like there is a sensor that measures the height above the surface but always returns zero since there is no height difference. In this way, a probability distribution is modeled that constrains the position to the surface. I investigate terrain maps with the practical example of track cycling. I show that terrain maps can yield full observability of the position and orientation; in other words, that they are able to correct the growing error of the inertial sensor. Thereby, only the curved parts of the track yield information about the position. As a result, the position can be tracked during 10km drives with an error of 1:08m (RMSE). Event-domain maps are a particular type of maps that specify where activities can be performed. For example, it is only possible to climb stairs at staircases. I investigate this type of knowledge at bouldering, where the climbers grip the holds of a route. The map represents a probability distribution of possible grip positions. I develop a two-step method where the first step estimates the transition between two holds. In a second step, the transitions are refined using the event-domain map. The estimated error improves from 0:266m (median) to 0:132m compared to an integrating solution without a map. Overall, modeling the three types of prior knowledge successfully reduces the drift in all cases. The human gait pattern can be utilized with a new kind of state estimator, which needs further investigation. The map-based types of knowledge correct the drift of the inertial sensor in the experiments. For the terrain map, it is even possible to prove the correction mathematically. This shows that prior knowledge modeled as prior distribution is effective to estimate the position solely with inertial sensors.
Download or read book Human Motion Tracking and Orientation Estimation Using Inertial Sensors and RSSI Measurements written by Ami Luttwak and published by . This book was released on 2011 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Technological Innovation for Collective Awareness Systems written by Luis M. Camarinha-Matos and published by Springer. This book was released on 2014-04-02 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2014, held in Costa de Caparica, Portugal, in April 2014. The 68 revised full papers were carefully reviewed and selected from numerous submissions. They cover a wide spectrum of topics ranging from collaborative enterprise networks to microelectronics. The papers are organized in the following topical sections: collaborative networks; computational systems; self-organizing manufacturing systems; monitoring and supervision systems; advances in manufacturing; human-computer interfaces; robotics and mechatronics, Petri nets; multi-energy systems; monitoring and control in energy; modelling and simulation in energy; optimization issues in energy; operation issues in energy; power conversion; telecommunications; electronics: design; electronics: RF applications; and electronics: devices.
Download or read book Multisensor Attitude Estimation written by Hassen Fourati and published by CRC Press. This book was released on 2016-11-03 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies’ orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications. This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level.
Download or read book Pose Estimation and Calibration Algorithms for Vision and Inertial Sensors written by Jeroen Hol and published by . This book was released on 2008 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with estimating position and orientation in real-time, using measurements from vision and inertial sensors. A system has been developed to solve this problem in unprepared environments, assuming that a map or scene model is available. Compared to 'camera-only' systems, the combination of the complementary sensors yields an accurate and robust system which can handle periods with uninformative or no vision data and reduces the need for high frequency vision updates.
Download or read book CONTROLO 2014 Proceedings of the 11th Portuguese Conference on Automatic Control written by António Paulo Moreira and published by Springer. This book was released on 2014-08-14 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last 20 years the Portuguese association of automatic control, Associação Portuguesa de Controlo Automático, with the sponsorship of IFAC have established the CONTROLO conference as a reference international forum where an effective exchange of knowledge and experience amongst researchers active in various theoretical and applied areas of systems and control can take place, always including considerable space for promoting new technical applications and developments, real-world challenges and success stories. In this 11th edition the CONTROLO conference evolved by introducing two strategic partnerships with Spanish and Brazilian associations in automatic control, Comité Español de Automática and Sociedade Brasileira de Automatica, respectively.
Download or read book Statistical Sensor Fusion written by Christian Lundquist and published by . This book was released on 2015-04-02 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Strapdown Inertial Navigation Technology written by David Titterton and published by IET. This book was released on 2004 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inertial navigation is widely used for the guidance of aircraft, missiles ships and land vehicles, as well as in a number of novel applications such as surveying underground pipelines in drilling operations. This book discusses the physical principles of inertial navigation, the associated growth of errors and their compensation. It draws current technological developments, provides an indication of potential future trends and covers a broad range of applications. New chapters on MEMS (microelectromechanical systems) technology and inertial system applications are included.
Download or read book An Alternative Sensor Fusion Method for Object Orientation Using Low Cost Mems Inertial Sensors written by Joshua L. Bouffard and published by . This book was released on 2016 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis develops an alternative sensor fusion approach for object orientation using low-cost MEMS inertial sensors. The alternative approach focuses on the unique challenges of small UAVs. Such challenges include the vibrational induced noise onto the accelerometer and bias offset errors of the rate gyroscope. To overcome these challenges, a sensor fusion algorithm combines the measured data from the accelerometer and rate gyroscope to achieve a single output free from vibrational noise and bias offset errors. One of the most prevalent sensor fusion algorithms used for orientation estimation is the Extended Kalman filter (EKF). The EKF filter performs the fusion process by first creating the process model using the nonlinear equations of motion and then establishing a measurement model. With the process and measurement models established, the filter operates by propagating the mean and covariance of the states through time. The success of EKF relies on the ability to establish a representative process and measurement model of the system. In most applications, the EKF measurement model utilizes the accelerometer and GPS-derived accelerations to determine an estimate of the orientation. However, if the GPS-derived accelerations are not available then the measurement model becomes less reliable when subjected to harsh vibrational environments. This situation led to the alternative approach, which focuses on the correlation between the rate gyroscope and accelerometer-derived angle. The correlation between the two sensors then determines how much the algorithm will use one sensor over the other. The result is a measurement that does not suffer from the vibrational noise or from bias offset errors.
Download or read book A Novel Relative Positioning Estimation System RPES Using MEMS based Inertial Sensors written by Hani Balkhair and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of MEMS-based inertial sensors for a relative positioning estimation system (RPES) was investigated. A number of data acquisition and processing techniques are developed and tested, to determine which one would provide the best performance of the proposed method. Because inertial-based sensors don't rely on other references to calibrate their position and orientation, there is a steady accumulation of errors over time. The errors are caused by various sources of noise such as temperature and vibration, and the errors are significant. This work investigates various methods to increase the signal to-noise ratio, in order to develop the best possible RPES method. The main areas of this work are as follows: (i) The proposed RPES application imposes specific boundary conditions to the signal processing, to increase the accuracy. (ii) We propose that using redundant inertial rate sensors would give a better performance over a single rate sensor. (iii) We investigate three Kalman filter algorithms to accommodate different combinations of sensors: Parallel sensors arrangement, Series sensors arrangement, and compression arrangement. In implementing these three areas, the results show that there is much better improvement in the data in comparison to using regular averaging techniques.
Download or read book Advances in Communication Signal Processing VLSI and Embedded Systems written by Shubhakar Kalya and published by Springer Nature. This book was released on 2019-11-30 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises selected peer-reviewed papers from the International Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems (VSPICE-2019). The contents are divided into five broad topics - VLSI and embedded systems, signal processing, power systems, illumination and control, and communication and networking. The book focuses on the latest innovations, trends, and challenges encountered in the different areas of electronics and communication, and electrical engineering. It also offers potential solutions and provides an insight into various emerging areas such as image fusion, bio-sensors, and underwater sensor networks. This book can prove to be useful for academics and professionals interested in the various sub-fields of electronics and communication engineering.
Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2015-02-02 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Download or read book Drift Reduction for Inertial Sensor Based Orientation and Position Estimation in the Presence of High Dynamic Variability During Competitive Skiing and Daily Life Walking written by Benedikt Fasel and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: drift correction ; inertial sensors ; sensor fusion ; measurements ; kinematics ; locomotion ; sports ; alpine skiing ; cross-country skiing ; walking.
