Showing papers on "Inertial navigation system published in 2014"

Consistency Analysis and Improvement of Vision-aided Inertial Navigation

Abstract: In this paper, we study estimator inconsistency in vision-aided inertial navigation systems (VINS) from the standpoint of system's observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, which results in smaller uncertainties, larger estimation errors, and divergence. We develop an observability constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. This framework is applicable to several variants of the VINS problem such as visual simultaneous localization and mapping (V-SLAM), as well as visual-inertial odometry using the multi-state constraint Kalman filter (MSC-KF). Our analysis, along with the proposed method to reduce inconsistency, are extensively validated with simulation trials and real-world experimentation.

Foot-mounted inertial navigation made easy

Abstract: Despite being around for almost two decades, foot-mounted inertial navigation only has gotten a limited spread. Contributing factors to this are lack of suitable hardware platforms and difficult system integration. As a solution to this, we present an open-source wireless foot-mounted inertial navigation module with an intuitive and significantly simplified dead reckoning interface. The interface is motivated from statistical properties of the underlying aided inertial navigation and argued to give negligible information loss. The module consists of both a hardware platform and embedded software. Details of the platform and the software are described, and a summarizing description of how to reproduce the module are given. System integration of the module is outlined and finally, we provide a basic performance assessment of the module. In summary, the module provides a modularization of the foot-mounted inertial navigation and makes the technology significantly easier to use.

High-fidelity sensor modeling and self-calibration in vision-aided inertial navigation

Abstract: In this paper, we propose a high-precision pose estimation algorithm for systems equipped with low-cost inertial sensors and rolling-shutter cameras. The key characteristic of the proposed method is that it performs online self-calibration of the camera and the IMU, using detailed models for both sensors and for their relative configuration. Specifically, the estimated parameters include the camera intrinsics (focal length, principal point, and lens distortion), the readout time of the rolling-shutter sensor, the IMU’s biases, scale factors, axis misalignment, and g-sensitivity, the spatial configuration between the camera and IMU, as well as the time offset between the timestamps of the camera and IMU. An additional contribution of this work is a novel method for processing the measurements of the rolling-shutter camera, which employs an approximate representation of the estimation errors, instead of the state itself. We demonstrate, in both simulation tests and real-world experiments, that the proposed approach is able to accurately calibrate all the considered parameters in real time, and leads to significantly improved estimation precision compared to existing approaches.

Nuclear magnetic resonance gyro for inertial navigation

Abstract: Since its discovery by Isidor Rabi in 1938 and his subsequent Nobel Prize in physics in 1944, scientists have been using nuclear magnetic resonance (NMR) technologies as a tool in analytic chemistry, biochemistry, and the study of atomic interactions. In 1952 General Electric proposed that a gyro could be made based on NMR technology, in particular the concept of using the intrinsic quantum property of spin was of interest, specifically the stability of a quantized angular momentum of a nucleus when subjected to a stable magnetic field. From roughly 1952 to 1980 several groups worked on the concept and development of an NMR based gyro, with some being more successful than others. While the fundamentals of the technology were understood and the concept demonstrated, the enabling technologies required to develop a compact, robust design that would operate outside of the controlled laboratory environment were not available. For the past several years Northrop Grumman has been investigating and developing a NMR-Gyro. Owing to the advancement in enabling technologies a small robust gyro package can now be produced. The current micro-NMRG design is housed in a 10 cubic centimeter package and has been tested over a limited environment. Projected size estimate for a 6 degree-of-freedom inertial measurement unit is on the order of 300 cc with a power draw of a few watts. Initial testing of the unit has shown a performance level better than any MEMS device currently available as well as approaching the performance of many fiber optic gyros. The micro-NMR-Gyro has the potential to provide the end user a high-performance device in a small robust package. Presented in this paper is a summary of the basic principles of operation and performance testing results of the hardware to date.

A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System

Abstract: The authors demonstrate the practical application of the adaptive high-gain extended Kalman filter (EKF) (AEKF) onboard a quadcopter unmanned aerial vehicle (UAV). The AEKF presents several advantages in state estimation, as it combines good filtering properties with an increased sensitivity to large perturbations. It does this by varying the high-gain parameter according to a metric called innovation. Unlike many adaptive observers, the AEKF is mathematically proven to globally converge, a significant advantage over the traditional EKF when considering robust controls. The AEKF is implemented on the UAV's inertial navigation system (INS). Full INSs can have problems when sensors are noisy and limited, particularly in the case of highly dynamically unstable systems such as a quadcopter. Simulation and experimental data show that the AEKF is suitable for this INS.

An Integrated Vehicle Navigation System Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult Environments for GPS

Abstract: A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.

GPS spoofing detection using RAIM with INS coupling

Abstract: In this work, we develop, implement, and test a monitor to detect GPS spoofing attacks using residual-based Receiver Autonomous Integrity Monitoring (RAIM) with inertial navigation sensors. Signal spoofing is a critical threat to all navigation applications that utilize GNSS, and is especially hazardous in aviation applications. This work develops a new method to directly detect spoofing using a GPS/INS integrated navigation system that incorporates fault detection concepts based on RAIM. The method is also capable of providing an upper bound on the proposed monitor's integrity risk.

Hybridizing matter-wave and classical accelerometers

Abstract: We demonstrate a hybrid accelerometer that benefits from the advantages of both conventional and atomic sensors in terms of bandwidth (DC to 430 Hz) and long term stability. First, the use of a real time correction of the atom interferometer phase by the signal from the classical accelerometer enables to run it at best performances without any isolation platform. Second, a servo-lock of the DC component of the conventional sensor output signal by the atomic one realizes a hybrid sensor. This method paves the way for applications in geophysics and in inertial navigation as it overcomes the main limitation of atomic accelerometers, namely the dead times between consecutive measurements.

Hybridizing matter-wave and classical accelerometers

Abstract: We demonstrate a hybrid accelerometer that benefits from the advantages of both conventional and atomic sensors in terms of bandwidth (DC to 430 Hz) and long term stability. First, the use of a real time correction of the atom interferometer phase by the signal from the classical accelerometer enables to run it at best performance without any isolation platform. Second, a servo-lock of the DC component of the conventional sensor output signal by the atomic one realizes a hybrid sensor. This method paves the way for applications in geophysics and in inertial navigation as it overcomes the main limitation of atomic accelerometers, namely, the dead times between consecutive measurements.

A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Abstract: Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises. The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster-Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20-87% improvement in the positional accuracy was found against ANN and RFR.

A New Technique for INS/GNSS Attitude and Parameter Estimation Using Online Optimization

Abstract: Integration of inertial navigation system (INS) and global navigation satellite system (GNSS) is usually implemented in engineering applications by way of Kalman-like filtering. This form of INS/GNSS integration is prone to attitude initialization failure, especially when the host vehicle is moving freely. This paper proposes an online constrained-optimization method to simultaneously estimate the attitude and other related parameters including GNSS antenna's lever arm and inertial sensor biases. This new technique benefits from self-initialization in which no prior attitude or sensor measurement noise information is required. Numerical results are reported to validate its effectiveness and prospect in high accurate INS/GNSS applications.

Distributed Indoor Positioning System With Inertial Measurements and Map Matching

Abstract: Accurate position information is nowadays very important in many applications. For instance, maintaining the situation awareness in command center in emergency operations is very crucial. Due to signal strength attenuation and multipath, Global Navigation Satellite Systems are not suitable for indoor navigation purposes. Radio network-based positioning techniques, such as wireless local area network, require local infrastructure that is often vulnerable in emergency situations. We propose here a distributed system for personal positioning based on inertial sensors. The system consists of an inertial measurement unit (IMU) connected to a radio carried by a person and the server connected to another radio. Step length and heading estimation is computed in the IMU and sent to the server. On the server side, the position is estimated using particle filter-based map matching. The benefit of the distributed architecture is that the computational capacity can be kept very low on the user side, which leads to long operation time as power consumption also remains very low.

Observability-constrained vision-aided inertial navigation

Abstract: This disclosure describes techniques for reducing or eliminating estimator inconsistency in vision-aided inertial navigation systems (VINS). For example, an observability-constrained VINS (OC-VINS) is described which enforce the unobservable directions of the system to prevent spurious information gain and reduce inconsistency.

A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

Abstract: Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

Abstract: The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

A multi-position self-calibration method for dual-axis rotational inertial navigation system

Abstract: In order to compensate errors of inertial measurement unit which is the core of rotational inertial navigation system, self-calibration is utilized as an effective way to reduce navigation error. Error model of navigation solution and initial alignment is used to establish the relationship between navigation errors and inertial measurement unit (IMU) errors. A second order damper is added to the vertical velocity channel to suppress the divergence and then the vertical velocity error can be regarded as an effective observation to estimate the error parameters. Since the accuracy of the self-calibration method is susceptible to the positioning error of gimbals, total least squares (TLS) method is utilized in identification of the error parameters. Experimental results show that all of the twenty-one error parameters can be estimated with the proposed rotation scheme. Compared to least squares (LS) method, TLS method can improve the position accuracy of 8 h by 46.2%.

Vision-Aided Inertial Navigation Based on Ground Plane Feature Detection

Abstract: In this paper, a motion estimation approach is introduced for a vision-aided inertial navigation system. The system consists of a ground-facing monocular camera mounted on an inertial measurement unit (IMU) to form an IMU-camera sensor fusion system. The motion estimation procedure fuses inertial data from the IMU and planar features on the ground captured by the camera. The main contribution of this paper is a novel closed-form measurement model based on the image data and IMU output signals. In contrast to existing methods, our algorithm is independent of the underlying vision algorithm for image motion estimation such as optical flow algorithms for camera motion estimation. The algorithm has been implemented using an unscented Kalman filter, which propagates the current and the last state of the system updated in the previous measurement instant. The validity of the proposed navigation method is evaluated both by simulation studies and by real experiments.

Embedded Vehicle Dynamics Aiding for USBL/INS Underwater Navigation System

Abstract: This brief presents an embedded vehicle dynamics (VD) aiding technique to enhance position, velocity, and attitude error estimation in low-cost inertial navigation systems (INSs), with application to underwater vehicles The model of the VD provides motion information that is complementary to the INS and, consequently, the fusion of both systems allows for a comprehensive improvement of the overall navigation system performance In this brief, the specific VD equations of motion are directly embedded in an extended Kalman filter, as opposed to classical external vehicle models that act as secondary INSs A tightly-coupled inverted ultrashort baseline is also adopted to enhance position and attitude estimation using measurements of relative position of a transponder located in the vehicle mission area The improvement of the overall navigation system is assessed in simulation using a nonlinear model of the INFANTE autonomous underwater vehicle, resorting to extensive Monte Carlo runs that implement perturbed versions of the nominal dynamics The results show that the vehicle dynamics produce relevant performance enhancements, and that the accuracy of the system is robust to modeling uncertainties

Towards consistent visual-inertial navigation

Abstract: United States. Office of Naval Research (N00014-12-1- 0093, N00014-10-1-0936, N00014-11-1-0688, and N00014-13-1-0588)

Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors

Abstract: Pedestrian navigation is an important ingredient for efficient multimodal transportation, such as guidance within large transportation infrastructures. A requirement is accurate positioning of people in indoor multistory environments. To achieve this, maps of the environment play a very important role. Foot-SLAM is an algorithm based on the simultaneous localization and mapping (SLAM) principle that relies on human odometry, i.e., measurements of a pedestrian’s steps, to build probabilistic maps of human motion for such environments and can be applied using crowdsourcing. In this paper, we extend FootSLAM to multistory buildings following a Bayesian derivation. Our approach employs a particle filter and partitions the map space into a grid of adjacent hexagonal prisms with eight faces. We model the vertical component of the odometry errors using an autoregressive integrated moving average (ARIMA) model and extend the geographic tree-based data structure that efficiently stores the probabilistic map, allowing real-time processing. We present the multistory FootSLAM maps that were created from three data sets collected in different buildings (one large office building and two university buildings). Hereby, the user was only carrying a single foot-mounted inertial measurement unit (IMU). We believe the resulting maps to be strong evidence of the robustness of FootSLAM. This paper raises the future possibility of crowdsourced indoor mapping and accurate navigation using other forms of human odometry, e.g., obtained with the low-cost and nonintrusive sensors of a handheld smartphone.

Enhanced, Delay Dependent, Intelligent Fusion for INS/GPS Navigation System

Abstract: Low-cost navigation systems, deployed for ground vehicles' applications, are designed based on the loosely coupled fusion between the global positioning system (GPS) and the inertial measurement unit (IMU). However, low-cost GPS receivers provide the position and velocity of the vehicle at a lower sampling rate than the IMU-sampled vehicle dynamics. In addition, the GPS measurements might be missed or delayed due to the receiver's inability to lock on the signal or due to obstruction from neighboring vehicles or infrastructures. In this paper, an architecture based on an adaptive neuro-fuzzy inference system is proposed for fusing the GPS/IMU measurements. This integration incorporates the variable delay between the IMU and GPS signals as an additional input to the fusion system. In addition, once the GPS signal becomes available, the measurement is used as a correction reference value to provide an enhancement to the estimation accuracy. The performance of the proposed method is initially demonstrated using a GPS/IMU simulation environment. Subsequently, an experimental test is also conducted to validate the performance of the method.

An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas

Abstract: We describe an enhanced quality control algorithm for the MEMS-INS/GNSS integrated navigation system. It aims to maintain the system's reliability and availability during global navigation satellite system (GNSS) partial and complete data loss and disturbance, and hence to improve the system's performance in urban environments with signal obstructions, tunnels, bridges, and signal reflections. To reduce the inertial navigation system (INS) error during GNSS outages, the stochastic model of the integration Kalman filter (KF) is informed by Allan variance analysis and the application of a non-holonomic constraint. A KF with a fault detection and exclusion capability is applied in the loosely and tightly coupled integration modes to reduce the adverse influence of abnormal GNSS data. In order to evaluate the performance of the proposed navigation system, road tests have been conducted in an urban area and the system's reliability and integrity is discussed. The results demonstrate the effectiveness of different algorithms for reducing the growth of INS error.

Estimation of Information Sharing Error by Dynamic Deformation Between Inertial Navigation Systems

Abstract: Dynamic deformation of a vehicle and relative position of inertial navigation systems will cause large errors during information sharing. Such errors should be estimated and compensated effectively, or it will cause large errors to navigation information output. Therefore, the model between deformation angle and dynamic lever arm is established to verify that the influence is not coaxial but decussate. Then, estimation and compensation methods for deformation angle and dynamic lever arm are proposed with real-time closed-loop correction of the lever-arm length. Simulation results demonstrate that, for the estimation of misalignment angle, the proposed method has a faster convergence than the traditional method. Moreover, the deformation angle and dynamic lever-arm estimation also takes less time with a higher accuracy than the traditional method.

Golf Swing Motion Tracking Using Inertial Sensors and a Stereo Camera

Abstract: A golf swing motion tracking algorithm is proposed in which the golf club trajectory (position and velocity) and club face orientation information are given. Two sensors are used in the algorithm: an inertial sensor unit on the golf club, and a stereo camera that captures infrared light emitting diodes (LEDs), also on the golf club. During the address and impact golf swing phases, the camera captures the infrared LEDs on the golf club. Using the infrared LEDs as landmarks, the position and orientation of the golf club are computed. When the golf club is moving, an inertial navigation algorithm is used to compute the golf club trajectory. An indirect Kalman filter with nine states is used to combine the inertial sensor and vision data. The average position accuracy is about 3.6 cm and the maximum error is about 13.2 cm. The proposed system can be used to analyze golf swings quantitatively.

Accurate Human Navigation Using Wearable Monocular Visual and Inertial Sensors

Abstract: This paper presents a novel visual-inertial integration system for human navigation in free-living environments, where the measurements from wearable inertial and monocular visual sensors are integrated. The preestimated orientation, obtained from magnet, angular rate, and gravity sensors, is used to estimate the translation based on the data from the visual and inertial sensors. This has a significant effect on the performance of the fusion sensing strategy and makes the fusion procedure much easier, because the gravitational acceleration can be correctly removed from the accelerometer measurements before the fusion procedure, where a linear Kalman filter is selected as the fusion estimator. Furthermore, the use of preestimated orientation can help to eliminate erroneous point matches based on the properties of the pure camera translation and thus the computational requirements can be significantly reduced compared with the RANdom SAmple Consensus algorithm. In addition, an adaptive-frame rate single camera is selected to not only avoid motion blur based on the angular velocity and acceleration after compensation, but also to make an effect called visual zero-velocity update for the static motion. Thus, it can recover a more accurate baseline and meanwhile reduce the computational requirements. In particular, an absolute scale factor, which is usually lost in monocular camera tracking, can be obtained by introducing it into the estimator. Simulation and experimental results are presented for different environments with different types of movement and the results from a Pioneer robot are used to demonstrate the accuracy of the proposed method.

Map-aided localization with lateral perception

Abstract: Accurate localization of a vehicle is a challenging task as GPS available on the market are not designed for lane-level accuracy application. Although dead reckoning helps, cumulative errors from inertial sensors result in a integration drift. This paper presents a new method of localization based on sensors data fusion. An accurate digital map of the lane marking is used as a powerful additional sensor. Road markings are detected by processing two lateral cameras to estimate their distance to the vehicle. Coupled with the map data in a EKF filter it improves the ego-localization obtained with inertial and GPS measurements. The result is a vehicle localization at an ego-lane level of accuracy, with a lateral error of less than 10 centimeters.

An historical perspective on inertial navigation systems

Abstract: Inertial navigation provides a unique ability to know where one has been, where one is currently, and where one is going, given only a starting position. The laws of physics permit the sensing of dynamic motion without external information, making inertial systems impervious to jamming, masking, or spoofing. Measurements of six degrees of freedom are required - three linear accelerations, and three angular rates - to fully propagate the velocity, position, and orientation of the system. The first inertial sensors are traced to the early 19th century and specialized inertial guidance systems appeared in the 1940s, yet inertial navigation systems did not become commonplace until the 1960s. This is largely due to the fact that requirements for navigation accuracy inertial sensors - accelerometers and gyroscopes - are very challenging. In the past fifty years, significant evolutionary and revolutionary changes have taken place in the designs of inertial sensors and systems. These include the progression from fluid-filled to dry instruments and the transition from mechanically complex stabilized inertial platforms to computationally intensive strapdown systems. Gyroscopes have evolved from large mechanical devices to highly refined precision mechanical sensors. Optical rotation sensors such as the ring laser gyro and the fiber optic gyro have enabled new system designs and capabilities. Coriolis vibratory gyroscopes such as the hemispherical resonator gyro are capable of extreme accuracy and reliability; new opportunities for miniaturizing these types of sensors will lead to new classes of accuracy for inertial navigation systems. Advanced gyroscope technologies such as the nuclear magnetic resonance gyroscope which uses atomic spin to detect rotation have already been demonstrated to achieve navigation accuracy requirements. Cold atom technologies may also provide the opportunity for very high accuracy accelerometers and gyroscopes in the future. Inertial navigation technologies and applications of the past, present, and future are discussed.