scispace - formally typeset
Search or ask a question

Showing papers on "GPS/INS published in 2011"


Journal ArticleDOI
TL;DR: This study suggests the use of Input-Delayed Neural Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS location and velocity, respectively, which results in a more reliable positioning solution during long GPS outages.

208 citations


Journal ArticleDOI
TL;DR: A novel estimation method for fast initial coarse alignment of a ship's strapdown inertial attitude reference system using only inertial measurement unit (IMU) measurements for quasi-static alignment and IMU measurements with GPS aiding for moving-base alignment is presented.
Abstract: This paper presents a novel estimation method for fast initial coarse alignment of a ship's strapdown inertial attitude reference system using only inertial measurement unit (IMU) measurements for quasi-static alignment and IMU measurements with GPS aiding for moving-base alignment. Unlike several current techniques, the presented estimation method is effective with any initial attitude error. The estimator is based on the decomposition of the attitude quaternion into separate Earth motion, inertial rate, and alignment quaternions. The alignment quaternion is estimated using a minimum variance fit between loci of body- and navigation-frame velocity vectors using solutions to Wahba's problem. One set of vectors is derived from time integrals of measured vehicle motions, and the second set is derived from Earth motion and GPS data (when moving). For the case of quasi-static alignment, an algebraic expression for the covariance of the attitude estimate as a function of the variance of navigation-frame velocity disturbances is developed. It is shown that, by averaging and interleaving the velocity vectors, the resulting attitude estimate is improved over sequential sampling techniques. It is further shown, for a maneuvering vessel, that a continuous estimate of the attitude error covariance can be generated from the IMU data. This latter feature allows direct initialization of a follow-on fine-alignment stochastic estimator's covariance matrix. Results are presented for quasi-static alignment using inertial sensors only and for full in-motion alignment using navigation-frame GPS velocity and position aiding.

169 citations


01 Jan 2011
TL;DR: This article describes a method of navigation for an individual based on traditional inertial navigation system technology, but with very small and self-contained sensor systems, to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth���s magnetic field are significant.
Abstract: This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step — the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth’s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically deter

125 citations


Journal ArticleDOI
TL;DR: In this paper, the authors describe a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems.
Abstract: This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step ��� the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth���s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically determine when sensed magnetic heading is accurate enough to be useful for additional error compensation.

108 citations


Journal ArticleDOI
TL;DR: This paper discusses mobile robot position estimation without using external signals in indoor environments and proposes a Kalman filter that estimates the orientation and velocity of mobile robots, which combines INS and odometry and delivers more accurate position information than standalone odometry.
Abstract: Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

96 citations


Journal ArticleDOI
TL;DR: In this paper, an extensive overview of existing vehicle dynamic state estimators that utilize the in-car Inertial Navigation Sensors and Global Positioning System is provided, with an emphasis on vehicle sidelip estimation.
Abstract: This paper provides an extensive overview of existing vehicle dynamic state estimators that utilise the in-car Inertial Navigation Sensors and Global Positioning System The different approaches are categorised, the techniques are summarised and the limitations and advantages of each approach are provided Recommendations for future research are also given The review is intended to be fairly comprehensive, but with an emphasis on vehicle sideslip estimation

94 citations


Journal ArticleDOI
Wei Gao1, Yueyang Ben1, Xin Zhang1, Qian Li1, Fei Yu1 
TL;DR: Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment and make use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence to quickly obtain the initial strapdown attitude matrix.
Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

87 citations


Journal ArticleDOI
TL;DR: In this article, an integrated Kalman filter (IKF) scheme was proposed to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity, assuming known vehicle parameters.

86 citations


Journal ArticleDOI
TL;DR: In this article, an enhanced version of the Particle Filter (PF) called Mixture PF is employed to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, and the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed.
Abstract: Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

84 citations


Journal ArticleDOI
TL;DR: A novel ground vehicle navigation system that combines INS, odometer and omnidirectional vision sensor that significantly reduces the accumulation of position, velocity and attitude errors during simulated GPS outages is proposed.
Abstract: Combining GPS/INS/odometer data has been considered one of the most attractive methodologies for ground vehicle navigation. In the case of long GPS signal blockages inherent to complex urban environments, however, the accuracy of this approach is largely deteriorated. To overcome this limitation, this study proposes a novel ground vehicle navigation system that combines INS, odometer and omnidirectional vision sensor. Compared to traditional cameras, omnidirectional vision sensors can acquire much more information from the environment thanks to their wide field of view. The proposed system automatically extracts and tracks vanishing points in omnidirectional images to estimate the vehicle rotation. This scheme provides robust navigation information: specifically by combining the advantages of vision, odometer and INS, we estimate the attitude without error accumulation and at a fast running rate. The accurate rotational information is fed back into a Kalman filter to improve the quality of the INS bridging in harsh urban conditions. Extensive experiments have demonstrated that the proposed approach significantly reduces the accumulation of position, velocity and attitude errors during simulated GPS outages. Specifically, the position accuracy is improved by over 30% during simulated GPS outages.

70 citations


Journal ArticleDOI
TL;DR: In this paper, the authors presented the first comprehensive analysis of the matrix square root calculations in the context of UKF and concluded that the Cholesky method is the best overall matrix square-root calculation for UKF applications in terms of performance and execution time.
Abstract: Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

Journal ArticleDOI
TL;DR: In this article, a modified inertial navigation system (INS) error dynamics is developed, and the quantization noise is incorporated into the modified INS error dynamics as augmenting driving noise.
Abstract: In this paper, modeling approaches for quantization and colored noises have been proposed. To accommodate the quantization noise, a modified inertial navigation system (INS) error dynamics is developed in this paper, and the quantization noise is incorporated into the modified INS error dynamics as augmenting driving noise. The three kinds of colored noises are modeled by using an equivalent differential equation driven by a unit white noise, and a technique is developed in this paper to augment the Kalman Filter of GPS/INS integration using this equivalent differential equation. Experimental test results show that the proposed stochastic error modeling approaches for quantization and colored noises significantly improves the accuracies of the estimated inertial drifts and the navigation solutions.

Proceedings ArticleDOI
11 Apr 2011
TL;DR: It is shown that proposed Adaptive method has a better performance rather than conventional method and shows the effectiveness of the GPS/INS integrated system.
Abstract: This paper investigates on the development and implementation of a high integrity navigation system based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU) for land vehicle applications. The complementary properties of the GPS and the INS have motivated several works dealing with their fusion by using a Kalman Filter. The conventional kalman filter has a fix error covariance matrix in all times of processing. Multi-sensor based navigation system that is implemented in this paper is called data synchronization. Also, multi-rate operations that are compared with conventional Kalman filtering has fix error covariance matrix. Therefore, when GPS outage occurred we have improper treat by kalman filter. In this paper we present an Adaptive method instead of conventional methods. It is shown that proposed method has a better performance rather than conventional method. Experimental results show the effectiveness of the GPS/INS integrated system.

Patent
31 May 2011
TL;DR: In this paper, a system consisting of a GPS receiver, together with an inertial measurement unit (IMU) and an electronic distance meter (EDM) or an image capture device all mounted on a survey pole is presented.
Abstract: A method and system obtains precise survey-grade position data of target points in zones where precise GPS data cannot be obtained, due to natural or man-made objects such as foliage and buildings. The system comprises position measurement components such as a GPS receiver, together with an inertial measurement unit (IMU) and an electronic distance meter (EDM) or an image capture device all mounted on a survey pole. The system and method obtains GPS data when outside the zone and uses the other position measurement systems, such as the IMU, inside the zone to traverse to a target point. The EDM or the image capture device may be used within or without the zone to obtain data to reduce accumulated position errors.

Journal ArticleDOI
TL;DR: The algorithm is shown to statistically outperform a tightly coupled GPS/inertial navigation solution both in full GPS coverage and in extended GPS blackouts, and as a function of road type, filter likelihood models, bias models, and filter integrity tests.
Abstract: A map-aided localization approach using vision, inertial sensors when available, and a particle filter is proposed and empirically evaluated. The approach, termed PosteriorPose, uses a Bayesian particle filter to augment global positioning system (GPS) and inertial navigation solutions with vision-based measurements of nearby lanes and stop lines referenced against a known map of environmental features. These map-relative measurements are shown to improve the quality of the navigation solution when GPS is available, and they are shown to keep the navigation solution converged in extended GPS blackouts. Measurements are incorporated with careful hypothesis testing and error modeling to account for non-Gaussian and multimodal errors committed by GPS and vision-based detection algorithms. Using a set of data collected with Cornell's autonomous car, including a measure of truth via a high-precision differential corrections service, an experimental investigation of important design elements of the PosteriorPose estimator is conducted. The algorithm is shown to statistically outperform a tightly coupled GPS/inertial navigation solution both in full GPS coverage and in extended GPS blackouts. Statistical performance is also studied as a function of road type, filter likelihood models, bias models, and filter integrity tests. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Journal ArticleDOI
26 May 2011-Sensors
TL;DR: This article presents a simple and low cost method to improve tractor positioning when only a GPS receiver is used as the positioning sensor and reveals that the proposed method effectively reduces the guidance GPS error along a straight trajectory.
Abstract: Error is always present in the GPS guidance of a tractor along a desired trajectory. One way to reduce GPS guidance error is by improving the tractor positioning. The most commonly used ways to do this are either by employing more precise GPS receivers and differential corrections or by employing GPS together with some other local positioning systems such as electronic compasses or Inertial Navigation Systems (INS). However, both are complex and expensive solutions. In contrast, this article presents a simple and low cost method to improve tractor positioning when only a GPS receiver is used as the positioning sensor. The method is based on placing the GPS receiver ahead of the tractor, and on applying kinematic laws of tractor movement, or a geometric approximation, to obtain the midpoint position and orientation of the tractor rear axle more precisely. This precision improvement is produced by the fusion of the GPS data with tractor kinematic control laws. Our results reveal that the proposed method effectively reduces the guidance GPS error along a straight trajectory.

Patent
18 Feb 2011
TL;DR: In this paper, a health management system is configured to compare the first set of kinematic state vectors and the second set of state vectors to determine faults associated with the accelerometer or the gyroscope based on the comparison.
Abstract: An inertial navigation system (INS) includes a primary inertial navigation system (INS) unit configured to receive accelerometer measurements from an accelerometer and angular velocity measurements from a gyroscope. The primary INS unit is further configured to receive global navigation satellite system (GNSS) signals from a GNSS sensor and to determine a first set of kinematic state vectors based on the accelerometer measurements, the angular velocity measurements, and the GNSS signals. The INS further includes a secondary INS unit configured to receive the accelerometer measurements and the angular velocity measurements and to determine a second set of kinematic state vectors of the vehicle based on the accelerometer measurements and the angular velocity measurements. A health management system is configured to compare the first set of kinematic state vectors and the second set of kinematic state vectors to determine faults associated with the accelerometer or the gyroscope based on the comparison.

Proceedings ArticleDOI
27 Jun 2011
TL;DR: This project is aim to design and implement a low cost Global Positioning System suitable to be used for hiking, climbing and sailing activities and overall results give an average of 10 % different compared with ideal theoretical calculated results.
Abstract: Stand alone global positioning system receivers are widely used nowadays to accurately locating one's position. By using stand alone GPS receivers the distance between two locations on earth can also be measured. This project is aim to design and implement a low cost Global Positioning System suitable to be used for hiking, climbing and sailing activities. The function of the GPS is to locate the position of user. The effects of line of sights in relation to different experimented locations are also studied. In this project, the hardware used is PIC18F4520 integrated with GPS receiver typed FV-M8. The GPS modules will generate the coordinates of latitude and longitude as well as the bearing angles between two positions. The algorithm to calculate the distance between two positions was developed by using PIC C Compiler. The written algorithm extracted the data from the GPS receiver via the RS232 communication. Microcontroller is used to parse the NMEA data sentences and execute the algorithm. Finally, the output is displayed to a LCD display unit. System testing conducted showed that for a few chosen different locations, geographical view and weather conditions, overall results give an average of 10 % different compared with ideal theoretical calculated results.

Journal ArticleDOI
TL;DR: This paper investigates augmenting GPS with a low-quality micro electro-mechanical system (MEMS) inertial measurement unit (IMU) and aircraft dynamic model (ADM) using a multiple model fusion strategy with the normalized solution separation (NSS) FD scheme.
Abstract: Approaches with vertical guidance (APV) can provide greater safety and cost savings to general aviation through accurate GPS horizontal and vertical navigation. However, GPS needs augmentation to achieve APV fault detection (FD) requirements. Aircraft-based augmentation systems (ABAS) fuse GPS with additional sensors at the aircraft. Typical ABAS designs assume high-quality inertial sensors with Kalman filters but these are too expensive for general aviation. Instead of using high-quality (and expensive) sensors, the purpose of this paper is to investigate augmenting GPS with a low-quality micro electro-mechanical system (MEMS) inertial measurement unit (IMU) and aircraft dynamic model (ADM). The IMU and ADM are fused together using a multiple model fusion strategy in a bank of extended Kalman filters (EKF) with the normalized solution separation (NSS) FD scheme. A tightly-coupled configuration with GPS is used and frequent GPS updates are applied to the IMU and ADM to compensate for their errors. Based upon a simulated APV approach, the performance of this architecture in detecting a GPS ramp fault is investigated, showing a performance improvement over a GPS-only "snapshot" implementation of the NSS method. The effect of fusing the IMU with the ADM is evaluated by comparing a GPS-IMU-ADM EKF with a GPS-IMU EKF, where a small improvement in protection levels is shown.

Journal ArticleDOI
18 Jul 2011-Sensors
TL;DR: Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.
Abstract: A land-based mobile mapping system (MMS) is flexible and useful for the acquisition of road environment geospatial information. It integrates a set of imaging sensors and a position and orientation system (POS). The positioning quality of such systems is highly dependent on the accuracy of the utilized POS. This limitation is the major drawback due to the elevated cost associated with high-end GPS/INS units, particularly the inertial system. The potential accuracy of the direct sensor orientation depends on the architecture and quality of the GPS/INS integration process as well as the validity of the system calibration (i.e., calibration of the individual sensors as well as the system mounting parameters). In this paper, a novel single-step procedure using integrated sensor orientation with relative orientation constraint for the estimation of the mounting parameters is introduced. A comparative analysis between the proposed single-step and the traditional two-step procedure is carried out. Moreover, the estimated mounting parameters using the different methods are used in a direct geo-referencing procedure to evaluate their performance and the feasibility of the implemented system. Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.

Journal ArticleDOI
TL;DR: In this article, the authors presented a high-accuracy, multirate inertial navigation system integrating global positioning system (GPS) measurements and advanced vector aiding techniques for precise position and attitude estimation of autonomous surface crafts (ASCs).
Abstract: This paper presents a high-accuracy, multirate inertial navigation system (INS) integrating global positioning system (GPS) measurements and advanced vector aiding techniques for precise position and attitude estimation of autonomous surface crafts (ASCs). Designed to be implemented and tested in the DELFIMx catamaran developed at ISR/IST, the navigation system comprises an advanced inertial integration algorithm to account for coning and sculling motions, combined with an extended Kalman filter (EKF) for inertial sensor error compensation. Aiding gravitational observations are optimally exploited in the EKF, by deriving a sensor integration technique that takes into account the vehicle's dynamics bandwidth information to properly trace measurement disturbances and extract the relevant sensor information. The proposed aiding technique and the performance of the navigation system are assessed using experimental data obtained at seatrials with a low-cost hardware architecture installed on-board the DELFIMx platform. It is shown that the low frequency information embodied in pendular measurements improves the compensation of inertial sensor bias and noise, and consequently enhances the performance of position and attitude estimation. The overall improvements obtained with the vector aiding observations are also illustrated for the case of GPS signal outage, emphasizing the extended autonomy of the navigation system with respect to position aiding.

Journal Article
TL;DR: In this article, the performance of a GPS/INS integration system is greatly determined by the ability of stand-alone inertial sensors to determine position and attitude within GPS outage due to sensor errors.
Abstract: The performance of a GPS/INS integration system is greatly determined by the ability of stand-alone INS system to determine position and attitude within GPS outage. The positional and attitude precision degrades rapidly during GPS outage due to INS sensor errors. With advantages of low price and volume, the Micro Electrical Mechanical Sensors (MEMS) have been wildly used in GPS/INS integration. Moreover, standalone MEMS can keep a reasonable positional precision only a few seconds due to systematic and random sensor errors. General stochastic error sources existing in inertial sensors can be modelled as (IEEE STD 647, 2006) Quantization Noise, Random Walk, Bias Instability, Rate Random Walk and Rate Ramp. Here we apply different methods to analyze the stochastic sensor errors, i.e. autoregressive modelling, Gauss-Markov process, Power Spectral Density and Allan Variance. Then the tests on a MEMS based inertial measurement unit were carried out with these methods. The results show that different methods give similar estimates of stochastic error model parameters. These values can be used further in the Kalman filter for better navigation accuracy and in the Doppler frequency estimate for faster acquisition after GPS signal outage.

Journal ArticleDOI
02 Nov 2011-Sensors
TL;DR: This paper proposes and provides observability analysis for the general distributed accelerometers-based inertial measurement unit, and shows that the angular rate can be correctly estimated by general nonlinear state estimators such as an extended Kalman filter, except under certain extreme conditions.
Abstract: A distributed set of accelerometers based on the minimum number of 12 accelerometers allows for computation of the magnitude of angular rate without using the integration operation. However, it is not easy to extract the magnitude of angular rate in the presence of the accelerometer noises, and even worse, it is difficult to determine the direction of a rotation because the angular rate is present in its quadratic form within the inertial measurement system equations. In this paper, an extended Kalman filter scheme to correctly estimate both the direction and magnitude of the angular rate through fusion of the angular acceleration and quadratic form of the angular rate is proposed. We also provide observability analysis for the general distributed accelerometers-based inertial measurement unit, and show that the angular rate can be correctly estimated by general nonlinear state estimators such as an extended Kalman filter, except under certain extreme conditions.

Journal ArticleDOI
TL;DR: In this paper, a magnetic compass fault detection method for GPS/INS/Magnetic compass integrated navigation systems is proposed, where the fault is assumed to be caused by the hard iron and soft iron effect and modeled as an abrupt change in the magnetic compass output.
Abstract: This paper proposes a magnetic compass fault detection method for GPS/INS/Magnetic compass integrated navigation systems. The fault is assumed to be caused by the hard iron and soft iron effect and modeled as an abrupt change in the magnetic compass output. In order to detect the fault, a test statistic related with only azimuth error measurement is determined. When a fault is detected, the GPS/INS/Magnetic compass integrated navigation system is changed into a GPS/INS integrated navigation system mode. In order to show the validity of the proposed method, computer simulation and van testing are carried out. The simulation and van test results show that the proposed navigation system gives more accurate outputs than the GPS/INS/Magnetic compass without the proposed method.

Journal ArticleDOI
TL;DR: This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU).
Abstract: This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU). The data provided by the inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear Kalman filter implemented with the aid of MPC555 microcontroller. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the sensors used and the mathematical modeling of INS and the aiding sensors' errors. Therefore, the IMU data is fused with the GPS data to increase the accuracy of the integrated GPS/IMU system. The equations required for the local geographic frame mechanization are derived. The direction cosine matrix approach is selected to compute orientation angles and the unified mathematical framework is chosen for position/velocity algorithm computations. This selection resulted in significant reduction in mechanization errors. It is shown that the constructed GPS/IMU system is successfully implemented with an accurate and reliable performance.

Journal ArticleDOI
TL;DR: In this article, a method for intelligent vehicle localization and 3D mapping in urban environments using integration of stereovision and Real-Time Kinematic GPS (RTK-GPS) is presented.

Journal ArticleDOI
TL;DR: An improved localization algorithm based on the hierarchical federation of three measurement layers, i.e., GPS, INS, and visual localization, is proposed to overcome the shortcomings of GPS/INS integrated systems and demonstrates enhancement of the robustness and accuracy of localization in outdoor environments.
Abstract: GPS/INS integrated systems do not guarantee robustness and accuracy of localization, because GPS has vulnerability to external disturbances. However, the overall performance and reliability of the system can be significantly improved by fusing multiple sensors with a different operating principle. In outdoor environments where GPS may be blocked, there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper proposes an improved localization algorithm based on the hierarchical federation of three measurement layers, i.e., GPS, INS, and visual localization, to overcome the shortcomings of GPS/INS integrated systems. The proposed algorithm automatically switches the operation modes according to GPS status and a network of a ground-based reference station. A vocabulary tree with SURF is used in the visual localization method. In the data fusion of visual localization and INS, an asynchronous and time-delayed data fusion algorithm is presented because visual localization is always time-delayed compared with INS. By using DGPS to obtain the reference position under the dynamic conditions of the reference station, the restrictions of the conventional DGPS are overcome and all UGVs within WiBro communication range of the reference station can accurately estimate the position with a common GPS. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

Journal ArticleDOI
TL;DR: A unified approach to the ultra-tightly coupled GPS/INS Integrated navigation system works well and gives accurate navigation output with anti-jamming capability.
Abstract: A unified approach to the ultra-tightly coupled GPS/INS Integrated navigation system is proposed. It is shown that other methods of ultra-tightly coupled GPS/INS integrated navigation systems are included in the proposed framework, and some implementation issues are discussed. In order to show the validity of the proposed approach, position accuracy and anti-jamming capabilities of three methods of ultra-tightly-coupled GPS/INS integration system, included in the proposed framework, were compared. The comparisons were carried out through computer simulations and post-processing of data from a GNSS hardware simulator. The results show that the proposed approach works well and gives accurate navigation output with anti-jamming capability.

Journal ArticleDOI
TL;DR: This paper introduces an approach combining visual-based simultaneous localization and mapping (V-SLAM) and global positioning system (GPS) correction for accurate multi-sensor localization of an outdoor mobile robot in geo-referenced maps.
Abstract: This paper introduces an approach combining visual-based simultaneous localization and mapping (V-SLAM) and global positioning system (GPS) correction for accurate multi-sensor localization of an outdoor mobile robot in geo-referenced maps. The proposed framework combines two extended Kalman filters (EKF); the first one, referred to as the integration filter, is dedicated to the improvement of the GPS localization based on data from an inertial navigation system and wheels' encoders. The second EKF implements the V-SLAM process. The linear and angular velocities in the dynamic model of the V-SLAM EKF filter are given by the GPS/INS/Encoders integration filter. On the other hand, the output of the V-SLAM EKF filter is used to update the dynamics estimation in the integration filter and therefore the geo-referenced localization. This solution increases the accuracy and the robustness of the positioning during GPS outage and allows SLAM in less featured environments.

Journal ArticleDOI
TL;DR: This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence.
Abstract: Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under p...