Showing papers on "Inertial navigation system published in 2010"
03 May 2010
TL;DR: This work proposes an extension to this approach to vehicle localization that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles.
Abstract: Autonomous vehicle navigation in dynamic urban environments requires localization accuracy exceeding that available from GPS-based inertial guidance systems. We have shown previously that GPS, IMU, and LIDAR data can be used to generate a high-resolution infrared remittance ground map that can be subsequently used for localization [4]. We now propose an extension to this approach that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles. Specifically, we model the environment, instead of as a spatial grid of fixed infrared remittance values, as a probabilistic grid whereby every cell is represented as its own gaussian distribution over remittance values. Subsequently, Bayesian inference is able to preferentially weight parts of the map most likely to be stationary and of consistent angular reflectivity, thereby reducing uncertainty and catastrophic errors. Furthermore, by using offline SLAM to align multiple passes of the same environment, possibly separated in time by days or even months, it is possible to build an increasingly robust understanding of the world that can be then exploited for localization. We validate the effectiveness of our approach by using these algorithms to localize our vehicle against probabilistic maps in various dynamic environments, achieving RMS accuracy in the 10cm-range and thus outperforming previous work. Importantly, this approach has enabled us to autonomously drive our vehicle for hundreds of miles in dense traffic on narrow urban roads which were formerly unnavigable with previous localization methods.
615 citations
11 Mar 2010
TL;DR: This paper describes and implements a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking, which represents an extended PDR methodology (IEz+) valid for operation in indoor spaces with local magnetic disturbances.
Abstract: The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.
460 citations
21 Jun 2010
TL;DR: This paper proposes a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images which is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed.
Abstract: A common prerequisite for many vision-based driver assistance systems is the knowledge of the vehicle's own movement. In this paper we propose a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images. Our method is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed. The only assumption we make is a known camera geometry, where the calibration may also vary over time. We employ an Iterated Sigma Point Kalman Filter in combination with a RANSAC-based outlier rejection scheme which yields robust frame-to-frame motion estimation even in dynamic environments. A high-accuracy inertial navigation system is used to evaluate our results on challenging real-world video sequences. Experiments show that our approach is clearly superior compared to other filtering techniques in terms of both, accuracy and run-time.
456 citations
TL;DR: This paper considers the vehicle navigation problem for an autonomous underwater vehicle (AUV) with six degrees of freedom using an error state formulation of the Kalman filter, and proposes novel tightly coupled techniques for the incorporation of the LBL and DVL measurements.
Abstract: This paper considers the vehicle navigation problem for an autonomous underwater vehicle (AUV) with six degrees of freedom. We approach this problem using an error state formulation of the Kalman filter. Integration of the vehicle's high-rate inertial measurement unit's (IMU's) accelerometers and gyros allow time propagation while other sensors provide measurement corrections. The low-rate aiding sensors include a Doppler velocity log (DVL), an acoustic long baseline (LBL) system that provides round-trip travel times from known locations, a pressure sensor for aiding depth, and an attitude sensor. Measurements correct the filter independently as they arrive, and as such, the filter is not dependent on the arrival of any particular measurement. We propose novel tightly coupled techniques for the incorporation of the LBL and DVL measurements. In particular, the LBL correction properly accounts for the error state throughout the measurement cycle via the state transition matrix. Alternate tightly coupled approaches ignore the error state, utilizing only the navigation state to account for the physical latencies in the measurement cycle. These approaches account for neither the uncertainty of vehicle trajectory between interrogation and reply, nor the error state at interrogation. The navigation system also estimates critical sensor calibration parameters to improve performance. The result is a robust navigation system. Simulation and experimental results are provided.
358 citations
TL;DR: A new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem and the suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is demonstrated.
Abstract: The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.
222 citations
TL;DR: In this article, the role of integrated optics and photonic integrated circuit technology in the enhancement of gyroscope performance and compactness is broadly discussed, and the architecture of new slow-light integrated angular rate sensors is described.
Abstract: Photonics for angular rate sensing is a well-established research field having very
important industrial applications, especially in the field of strapdown inertial
navigation. Recent advances in this research field are reviewed. Results obtained in
the past years in the development of the ring laser gyroscope and the fiber optic
gyroscope are presented. The role of integrated optics and photonic integrated
circuit technology in the enhancement of gyroscope performance and compactness is
broadly discussed. Architectures of new slow-light integrated angular rate sensors
are described. Finally, photonic gyroscopes are compared with other solid-state
gyros, showing their strengths and weaknesses.
200 citations
BMW1
TL;DR: This work introduces a novel self-contained seamless positioning solution for indoor and outdoor environments, well suited for and designed to be operated on off-the-shelf mobile phones.
Abstract: We introduce a novel self-contained seamless positioning solution for indoor and outdoor environments, well suited for and designed to be operated on off-the-shelf mobile phones. Position information is deduced from a combination of GNSS where available, combined with Pedestrian Dead Reckoning (PDR) utilizing inertial measurements and context-aware activity based map matching. The proposed system heavily exploits different types of human movement, such as walking, running, ascending or descending stairs, to improve the employed positioning model. In remaining independent from any external infrastructure, accurate localization is also possible in environments, where the installation and maintenance of such infrastructure does not make sense or is simply not affordable - as for example in a parking garage to guide a user to the next exit or back to his car. Concerning this particular use case we have also implemented an interface for the synchronization of location information between the mobile positioning solution and the car.
194 citations
29 Nov 2010
TL;DR: The results show that for leveled ground forward gait at a speed of 5 km/h, the angular rate energy detector and the SHOE give the highest performance, with a position accuracy of 0.14% of the travelled distance.
Abstract: A study of the performance of four zero-velocity detectors for a foot-mounted inertial sensor based pedestrian navigation system is presented. The four detectors are the acceleration moving variance detector, the acceleration magnitude detector, the angular rate energy detector, and a novel generalized likelihood ratio test detector, refereed to as the SHOE. The performance of each detector is assessed by the accuracy of the position solution provided by the navigation system employing the detector to perform zero-velocity updates. The results show that for leveled ground forward gait at a speed of 5 km/h, the angular rate energy detector and the SHOE give the highest performance, with a position accuracy of 0.14% of the travelled distance. The results also indicate that during leveled ground forward gait, the gyroscope signals hold the most reliable information for zero-velocity detection.
193 citations
TL;DR: In this paper, a nonlinear observer (i.e., a filter) is proposed for estimating the attitude of a flying rigid body, using measurements from low-cost inertial and magnetic sensors.
184 citations
TL;DR: The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method and the experimental results show the performance of the proposed control algorithm.
Abstract: Quad-robot type (QRT) unmanned aerial vehicles (UAVs) have been developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV is equipped with four propellers driven by each electric motor, an embedded controller, an Inertial Navigation System (INS) using three rate gyros and accelerometers, a CCD (Charge Coupled Device) camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. Accurate modeling and robust flight control of QRT UAVs are mainly discussed in this work. Rigorous dynamic model of a QRT UAV is obtained both in the reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurately derived dynamics. The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method. Although a model is incorrect, DOB method can design a controller by regarding the inaccurate part of the model and sensor noises as disturbances. The UAV can also avoid obstacles using eight IR (Infrared) and four ultrasonic range sensors. This kind of micro UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment. The experimental results show the performance of the proposed control algorithm.
172 citations
TL;DR: A new zero detection algorithm is proposed in the paper, where only one Gyroscope value is used and a Markov model is constructed using segmentation of gyroscope outputs instead of using gyro scope outputs directly, which makes the zero velocity detection more reliable.
Abstract: In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.
TL;DR: A navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals is developed.
Abstract: In this paper, a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units is presented. The goal of this paper is to develop a navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero-velocity duration from the ground reaction sensors is used to reset the accumulated integration errors from accelerometers and gyroscopes in position calculation. With the described system, an average position error of 4 m is achieved at the end of half-hour walks.
TL;DR: A reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS provides a 2-D navigation solution, which is adequate for land vehicles.
Abstract: To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS This system provides a 2-D navigation solution, which is adequate for land vehicles The traditional technique for this multisensor integration problem is Kalman filtering (KF) Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions An enhanced version of PF is utilized in this paper and is called the Mixture PF Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift The experimental results show that the two proposed solutions outperform all the compared counterparts
Book•
31 Aug 2010
TL;DR: In this paper, the authors focus on the application of MEMS inertial sensors to navigation systems and show how to minimize cost by adding and removing inertial sensor nodes, and provide integration strategies with examples from real field tests.
Abstract: Due to their micro-scale size and low power consumption, Microelectromechanical systems (MEMS) are now being utilized in a variety of fields This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems The book shows you how to minimize cost by adding and removing inertial sensors Moreover, this practical reference provides you with various integration strategies with examples from real field tests From an introduction to MEMS navigation related applications to special topics on Alignment for MEMS-Based Navigation to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area
TL;DR: The proposed 3-D navigation solution using Mixture PF for RISS/GPS integration is examined by road-test trajectories in a land vehicle and shows that the proposed solution outperforms all the compared counterparts.
Abstract: Recent technological advances in both GPS and low-cost microelectromechanical-system (MEMS)-based inertial sensors have enabled the monitoring of the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. GPS is presently widely used in land vehicles. However, in some environments, the GPS signal may suffer from signal blockage and multipath effects that deteriorate the positioning accuracy. When miniaturized inside any moving platforms, the MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced inertial sensor system (RISS) with MEMS-based inertial sensors. In this paper, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle's odometer, and the whole system is integrated with GPS to obtain a 3-D navigation solution. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called Mixture PF. The performance of the proposed 3-D navigation solution using Mixture PF for RISS/GPS integration is examined by road-test trajectories in a land vehicle. The proposed method is compared with four other solutions: 1) 3-D solution using KF for full INS/GPS integration; 2) 2-D solution using KF for RISS/GPS integration; 3) 2-D solution using Mixture PF for RISS/GPS integration; and 4) 3-D solution using sampling/importance resampling (SIR) PF for RISS/GPS integration. The experimental results show that the proposed solution outperforms all the compared counterparts.
03 Dec 2010
TL;DR: This paper describes a method that has been developed to aid an inertial navigation system when GNSS signals are not available, by taking advantage of the uniqueness of magnetic field variations.
Abstract: This paper describes a method that has been developed to aid an inertial navigation system when GNSS signals are not available, by taking advantage of the uniqueness of magnetic field variations. Most indoor environments have many different features (ferrous structural materials or contents, electrical currents, etc.) which perturb the Earths natural magnetic field. The variations in the magnetic field in indoor environments can be used as a way to identify a users position, and possibly orientation, because the 3-dimensional magnetic field varies significantly as a function of position. Using relatively inexpensive 3-axis magnetic field sensors, it is possible to estimate a users location in an indoor environment.
03 Dec 2010
TL;DR: A visual odometry system with an aided inertial navigation filter is combined to produce a precise and robust navigation system that does not rely on external infrastructure and to handle uncertainties in the system in a principled manner.
Abstract: We combine a visual odometry system with an aided inertial navigation filter to produce a precise and robust navigation system that does not rely on external infrastructure. Incremental structure from motion with sparse bundle adjustment using a stereo camera provides real-time highly accurate pose estimates of the sensor which are combined with six degree-of-freedom inertial measurements in an Extended Kalman Filter. The filter is structured to neatly handle the incremental and local nature of the visual odometry measurements and to handle uncertainties in the system in a principled manner. We present accurate results from data acquired in rural and urban scenes on a tractor and a passenger car travelling distances of several kilometers.
29 Nov 2010
TL;DR: A comparison between different step length estimation algorithms for pedestrian dead reckoning is presented and the performance of the estimators' performance is evaluated for arbitrary placement of the sensors.
Abstract: A comparison between different step length estimation algorithms for pedestrian dead reckoning is presented. This work covers theoretic evaluation of the estimators' performance and presents a comparison based on measurement data. Measurement data were taken from a group of five adults walking at three different velocities. For reference, the sensors were placed according to the recommendation given for each algorithm. In respect to everyday usability the performance of the estimators is furthermore evaluated for arbitrary placement of the sensors, as it is the case when using a mobile measurement platform like a smartphone.
TL;DR: In this paper, a road grade estimation algorithm based on Kalman filter fusion of vehicle sensor data and GPS positioning information was proposed for look-ahead cruise controllers and other advanced driver assistance systems for heavy duty vehicles.
01 Mar 2010
TL;DR: In this paper, the authors describe the development of the MEMS sensor design and performance with a specific emphasis on the performance drivers and predictions of the future applications of the various sensor technologies.
Abstract: : For many navigation applications, improved accuracy/performance is not necessarily the most important issue, but meeting performance at reduced cost and size is In particular, small navigation sensor size allows the introduction of guidance, navigation, and control into applications previously considered out of reach (eg, artillery shells, guided bullets) Three major technologies have enabled advances in military and commercial capabilities: Ring Laser Gyros, Fiber Optic Gyros, and Micro-Electro-Mechanical Systems (MEMS) gyros and accelerometers RLGs and FOGs are now mature technologies, although there are still technology advances underway for FOGs MEMS is still a very active development area Technology developments in these fields are described with specific emphasis on MEMS sensor design and performance Some aspects of performance drivers are mentioned as they relate to specific sensors Finally, predictions are made of the future applications of the various sensor technologies
TL;DR: It is feasible to develop self-contained pedestrian tracking system using inertial/magnetic sensors, eliminating the need for complicated and normally expensive infrastructure that most existing tracking systems rely on.
Abstract: This paper presents a sensor-based pedestrian tracking technology that does not rely on any infrastructure. The information about human walking is monitored by a sensor module composed of accelerometers, gyroscopes and magnetometers. The acquired information is used by an algorithm proposed in this paper to accurately compute the position of a pedestrian. Through the application of human kinetics, the algorithm integrates two traditional technologies: strap-down inertial navigation and pedestrian dead-reckoning. Based on the algorithm, this paper presents several methods to improve the accuracy of pedestrian tracking through reducing the integral drift which is the main cause of errors in inertial navigation. These methods have been carefully investigated through theoretical study, simulation and field experiment. The results indicate accurate tracking is achievable through the application of both the proposed algorithm and methods. Evidently, it is feasible to develop self-contained pedestrian tracking system using inertial/magnetic sensors, eliminating the need for complicated and normally expensive infrastructure that most existing tracking systems rely on.
01 Mar 2010
TL;DR: Accuracy and other technology trends for inertial sensors, Global Positioning Systems (GPS), and integrated Inertial Navigation System (INS)/GPS systems, including considerations of interference, that will lead to better than 1 meter accuracy navigation systems of the future are focused on.
Abstract: This paper focuses on accuracy and other technology trends for inertial sensors, Global Positioning Systems (GPS), and integrated Inertial Navigation System (INS)/GPS systems, including considerations of interference, that will lead to better than 1 meter accuracy navigation systems of the future. For inertial sensors, trend-setting sensor technologies will be described. A vision of the inertial sensor instrument field and strapdown inertial systems for the future is given. Planned accuracy improvements for GPS are described. The trend towards deep integration of INS/GPS is described, and the synergistic benefits are explored. Some examples of the effects of interference are described, and expected technology trends to improve system robustness are presented.
TL;DR: The development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced and two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS.
Abstract: Currently, the concept of multisensor system integration is implemented in land-vehicle navigation (LVN) applications. The most common LVN multisensor configuration incorporates an integrated Inertial Navigation System/Global Positioning System (INS/GPS) system based on the Kalman filter (KF). For LVN, the demand is directed toward low-cost inertial sensors such as microelectromechanical systems (MEMS). Due to the combined problem of frequent GPS signal loss during navigation in urban centers and the rapid time-growing inertial navigation errors when the INS is operated in stand-alone mode, some methodologies should be applied to improve the LVN accuracy in these cases. One of these approaches is to apply smoothing algorithms such as the Rauch-Tung-Striebel smoother (RTSS), which uses only the output of the forward KF. In this paper, the development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced. Two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS.
TL;DR: ERAIM procedures are able to detect faults in the dynamic model and isolate them from the measurement model, and include outlier detection and identification capabilities, reliability and separability capabilities, in a tightly coupled scenario.
Abstract: The integration of globe navigation satellite system (GNSS) with inertial navigation system (INS) is being heavily investigated as it can deliver more robust and reliable systems than either of the individual systems. In order to ensure the integrity of navigation solutions, it is necessary to incorporate an effective quality control scheme which uses redundant information provided by both the measurement and dynamic models. As the GNSS receiver autonomous integrity monitoring (RAIM) algorithms are well developed, here they are adapted to integrated GNSS/INS systems referred as extended RAIM ( eRAIM ) , which are derived from the least-squares estimators of the state parameters in a Kalman filter, to assess GNSS/INS performance for a tightly coupled scenario. In addition to the RAIM capabilities, eRAIM procedures are able to detect faults in the dynamic model and isolate them from the measurement model. The analysis includes outlier detection and identification capabilities, reliability and separability m...
TL;DR: A new algorithm for estimating the relative translation and orientation of an inertial measurement unit and a camera, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it, which works well in practice, both for perspective and spherical cameras.
Abstract: This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving, for example, sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.
03 Nov 2010
TL;DR: This work presents AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks.
Abstract: We present AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks. A property owner embeds a small tag inside the asset to be protected, where the tag lies dormant until it detects vehicular movement. Once moved, the tag uses inertial sensor-based dead reckoning to estimate position changes, but to reduce integration errors, the relative position is reset whenever the sensors indicate the vehicle has stopped. The sequence of movements, stops, and turns are logged in compact form and eventually transferred to a server using a cellular modem after both sufficient time has passed (to avoid detection) and RF power is detectable (hinting cellular access may be available). Eventually, the trajectory data are sent to a server which attempts to match a path to the observations. The algorithm uses a Hidden Markov Model of city streets and Viterbi decoding to estimate the most likely path. The proposed design leverages low-power radios and inertial sensors, is immune to intransit cloaking, and supports post hoc path reconstruction. Our prototype demonstrates technical viability of the design; the volume market forces driving machine-to-machine communications will soon make the design economically viable.
TL;DR: The standard multi-position calibration method for consumer-grade IMUs using a rate table is enhanced to exploit also the centripetal accelerations caused by the rotation of the table, making the method less sensitive to errors and allowing use of more accurate error models.
Abstract: An accurate inertial measurement unit (IMU) is a necessity when considering an inertial navigation system capable of giving reliable position and velocity estimates even for a short period of time. However, even a set of ideal gyroscopes and accelerometers does not imply an ideal IMU if its exact mechanical characteristics (i.e. alignment and position information of each sensor) are not known. In this paper, the standard multi-position calibration method for consumer-grade IMUs using a rate table is enhanced to exploit also the centripetal accelerations caused by the rotation of the table. Thus, the total number of measurements rises, making the method less sensitive to errors and allowing use of more accurate error models. As a result, the accuracy is significantly enhanced, while the required numerical methods are simple and efficient. The proposed method is tested with several IMUs and compared to existing calibration methods.
TL;DR: In this paper, a research study aimed at developing a novel indoor positioning system was presented, which used sensor fusion techniques to combine information from two sources: an in-house and an external source.
Abstract: A research study aimed at developing a novel indoor positioning system is presented. The realized system prototype uses sensor fusion techniques to combine information from two sources: an in-house ...
10 Dec 2010
TL;DR: This paper overviews the main concepts related to TBN and presents an exhaustive survey of the works reported in the literature, including a table comparing the motion and the measurement models, as well as the probabilistic framework used for the estimation.
Abstract: Terrain Based Navigation (TBN) is a method rooted to the early cruise missile navigation systems, when GPS was not yet available. For decades, TBN has been applied as a complementary system to INS navigation for Unmanned Aerial Vehicles (UAV). In the field of Autonomous Underwater Vehicles (AUVs), it has the potential to bound the drift inherent to dead reckoning navigation, based on INS and/or Doppler Velocity Log (DVL) sensors, as well as to make the navigation beyond the areas of coverture of the acoustic transponder networks, a reality. This paper overviews the main concepts related to TBN and present an exhaustive survey of the works reported in the literature. As a main contribution, a table comparing the motion and the measurement models, as well as the probabilistic framework used for the estimation is reported. An effort has been put on unifying the diverse nomenclature used across the surveyed works. We aim this paper to become an starting point for the researchers interested in this technology, with pointers to the most interested works in the area.
03 Dec 2010
TL;DR: A shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application is proposed.
Abstract: Heading drift error remains a problem in a standalone navigation system that uses only low cost MEMS IMU due to yaw error unobservability. This paper therefore proposes a shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application. There were no additional sensors used except MEMS IMU that contains accelerometers and gyros. Two trials; represented by regular and irregular walking trials, were undertaken in a typical public building. The results were then compared with HSGPS solution and IMU+ZUPT solution. Based on these trials, return position error of 0.1% from total distance travelled was achieved using a low cost MEMS IMU only.