Showing papers on "Inertial navigation system published in 2021"
TL;DR: The proposed SL-SRCKF strategy is a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter that comprises two cycle filtering systems that work in a tightly coupled mode and allows more accurate error correction results to be obtained during GPS outages.
Abstract: To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.
111 citations
TL;DR: The PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors, multi-platform/multi-device/ multi-sensor information fusion, self-learning systems, and the integration with artificial intelligence, 5G, IoT, and edge/fog computing.
Abstract: This paper reviews the state of the art and future trends of indoor Positioning, Localization, and Navigation (PLAN). It covers the requirements, the main players, sensors, and techniques for indoor PLAN. Other than the navigation sensors such as Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), the environmental-perception sensors such as High-Definition map (HD map), Light Detection and Ranging (LiDAR), camera, the fifth generation of mobile network communication technology (5G), and Internet-of-Things (IoT) signals are becoming important aiding sensors for PLAN. The PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors, multi-platform/multi-device/multi-sensor information fusion, self-learning systems, and the integration with artificial intelligence, 5G, IoT, and edge/fog computing.
96 citations
TL;DR: In this article, a semi-tight coupling framework of multi-GNSS PPP and stereo VINS (S-VINS) is presented to achieve the bidirectional location transfer and sharing in two separate navigation systems.
Abstract: Because of its high-precision, low-cost and easy-operation, Precise Point Positioning (PPP) becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones. However, the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System (GNSS) signals are blocked frequently. Inertial Navigation System (INS) has been integrated with GNSS to ameliorate such situations in the last decades. Recently, the Visual-Inertial Navigation Systems (VINS) with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only. Nevertheless, the system still must rely on the global positions to eliminate the accumulated errors. In this contribution, we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS (S-VINS), which achieves the bidirectional location transfer and sharing in two separate navigation systems. In our approach, the local positions, produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method. Furthermore, the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments. The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode. We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment. For the complex driving environment, the PPP positioning capability is significantly improved with the aiding of S-VINS. The 3D positioning accuracy is improved by 49.0% for Global Positioning System (GPS), 40.3% for GPS + GLOANSS (Global Navigation Satellite System), 45.6% for GPS + BDS (BeiDou navigation satellite System), and 51.2% for GPS + GLONASS + BDS. On this basis, the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8–60.6% in 3D positioning accuracy compared with the multi-GNSS PPP/INS solutions.
63 citations
TL;DR: In this paper, an improved extended Kalman filter (IEKF) was used to eliminate the influence of noises in MEMS-INS and mitigate dependence on the process model, and a deep learning framework with multiple long short-term memory (LSTM) modules was proposed to predict the increment of the vehicle position based on Gaussian mixture model (GMM) and Kullback-Leibler (KL) distance.
Abstract: Integration of microelectromechanical system-based inertial navigation system (MEMS-INS) and global positioning system (GPS) is a promising approach to vehicle localization. However, such a scheme may have poor performance during GPS outages and is less robust to measurement noises in changeable urban environments. In this article, we give an improved extended Kalman filter (IEKF) using an adaptation mechanism to eliminate the influence of noises in MEMS-INS and mitigate dependence on the process model. Especially, to guarantee accurate position estimation of the INS, a deep learning framework with multiple long short-term memory (multi-LSTM) modules is proposed to predict the increment of the vehicle position based on Gaussian mixture model (GMM) and Kullback–Leibler (KL) distance. The IEKF and the multi-LSTM are then combined together to optimize vehicle positioning accuracy during GPS outages in changeable urban environments. Numerical simulations and real-world experiments have demonstrated the effectiveness of the combined IEKF and multi-LSTM method, with the root-mean-square error (RMSE) of predicted position reduced by up to 93.9%. Or specifically, the RMSEs during GPS outages with durations 30, 60, and 120 s are 2.34, 2.69, and 3.08 m, respectively, which obviously outperform the existing method.
62 citations
TL;DR: This work addresses the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach and demonstrates that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution.
Abstract: Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.
56 citations
TL;DR: This review recounts the history of foot-mounted inertial navigation and characterize the main sources of error and systematically analyzes current approaches to robust zero-velocity detection, while categorizing public code and data.
Abstract: Fifteen years have passed since the publication of Foxlin’s seminal paper “Pedestrian tracking with shoe-mounted inertial sensors”. In addition to popularizing the zero-velocity update, Foxlin also hinted that the optimal parameter tuning of the zero-velocity detector is dependent on, for example, the user’s gait speed. As demonstrated by the recent influx of related studies, the question of how to properly design a robust zero-velocity detector is still an open research question. In this review, we first recount the history of foot-mounted inertial navigation and characterize the main sources of error, thereby motivating the need for a robust solution. Following this, we systematically analyze current approaches to robust zero-velocity detection, while categorizing public code and data. The article concludes with a discussion on commercialization along with guidance for future research.
55 citations
TL;DR: This paper proposes to break the cycle of continuous integration used in traditional inertial algorithms, formulate it as an optimization problem, and explore the use of deep recurrent neural networks for estimating the displacement of a user over a specified time window.
Abstract: Inertial measurement units (IMUs) have emerged as an essential component in many of today's indoor navigation solutions due to their low cost and ease of use. However, despite many attempts for reducing the error growth of navigation systems based on commercial-grade inertial sensors, there is still no satisfactory solution that produces navigation estimates with long-time stability in widely differing conditions. This paper proposes to break the cycle of continuous integration used in traditional inertial algorithms, formulate it as an optimization problem, and explore the use of deep recurrent neural networks for estimating the displacement of a user over a specified time window. By training the deep neural network using inertial measurements and ground truth displacement data, it is possible to learn both motion characteristics and systematic error drift. As opposed to established context-aided inertial solutions, the proposed method is not dependent on either fixed sensor positions or periodic motion patterns. It can reconstruct accurate trajectories directly from raw inertial measurements, and predict the corresponding uncertainty to show model confidence. Extensive experimental evaluations demonstrate that the neural network produces position estimates with high accuracy for several different attachments, users, sensors, and motion types. As a particular demonstration of its flexibility, our deep inertial solutions can estimate trajectories for non-periodic motion, such as the shopping trolley tracking. Further more, it works in highly dynamic conditions, such as running, remaining extremely challenging for current techniques.
51 citations
TL;DR: Experimental results presenting an unmanned aerial vehicle using terrestrial cellular SOPs to aid its onboard consumer-grade IMU in the absence of GNSS signals and demonstrating that the final position error of a traditional tightly coupled GNSS-aided INS after 30 s of GN SS cutoff was 57.30 m, while the final location error of the tightly coupled SOP- aided INS was 9.59 m.
Abstract: A tightly coupled inertial navigation system (INS) aided by ambient signals of opportunity (SOPs) is developed. In this system, a navigating vehicle aids its onboard INS using pseudoranges drawn from terrestrial SOPs with unknown emitter positions and clock biases through an extended Kalman filter-based radio simultaneous localization and mapping (SLAM) framework. The SOP-aided INS uses both global navigation satellite system (GNSS) and SOP pseudoranges during GNSS availability periods and switches to using SOP pseudoranges exclusively during GNSS unavailability periods. This framework is studied through numerical simulations by varying: 1) Quantity of exploited SOPs and 2) quality of SOP-equipped oscillators. It is demonstrated that the SOP-aided INS using a consumer-grade IMU produces smaller estimation uncertainties compared to a traditional tightly coupled GNSS-aided INS using a tactical-grade IMU. In the absence of GNSS signals, over the simulation finite-time horizon, the errors produced by the SOP-aided INS appear to be bounded, while the errors produced by a traditional tightly coupled GNSS-aided INS diverge unboundedly. Moreover, the article presents experimental results demonstrating an unmanned aerial vehicle using terrestrial cellular SOPs to aid its onboard consumer-grade IMU in the absence of GNSS signals. It is demonstrated that the final position error of a traditional tightly coupled GNSS-aided INS after 30 s of GNSS cutoff was 57.30 m, while the final position error of the tightly coupled SOP-aided INS was 9.59 m.
51 citations
TL;DR: The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level3-D altitude estimation accuracy in typical indoor environments.
Abstract: Indoor wireless localization using Bluetooth low energy (BLE) beacons has attracted considerable attention due to its extensive distribution and low cost properties. This article proposes a novel 3-D indoor localization algorithm which uses the combination of BLE and multiple sensors (3D-LBMS). The inertial navigation system (INS) and pedestrian dead reckoning (PDR) mechanizations are combined for accurate heading and speed estimation, which contains a multilevel constraints-based quasistatic magnetic field (QSMF) detection algorithm. In addition, dynamic-time-warping (DTW)-based BLE landmark detection algorithm is proposed to provide absolute 3-D location reference to multiple sensors-based positioning method, and the detected BLE landmark points are also used to calibrate the parameter of step-length calculation. Finally, the adaptive unscented Kalman filter (AUKF) is applied to fuse the results of INS/PDR mechanizations, QSMF and locations of detected BLE landmarks to achieve accurate and concrete multisource-based 3-D indoor localization performance. The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level 3-D altitude estimation accuracy in typical indoor environments.
44 citations
TL;DR: A proof-of-concept experiment, demonstrating rotation measurements on a rotating setup utilizing nuclear spins of an ensemble of nitrogen vacancy centers as a sensing element with no stationary reference.
Abstract: A rotation sensor is one of the key elements of inertial navigation systems and compliments most cell phone sensor sets used for various applications. Currently, inexpensive and efficient solutions are mechanoelectronic devices, which nevertheless lack long-term stability. Realization of rotation sensors based on spins of fundamental particles may become a drift-free alternative to such devices. Here, we carry out a proof-of-concept experiment, demonstrating rotation measurements on a rotating setup utilizing nuclear spins of an ensemble of nitrogen vacancy centers as a sensing element with no stationary reference. The measurement is verified by a commercially available microelectromechanical system gyroscope.
43 citations
TL;DR: Experimental results illustrate that the proposed algorithm could improve the navigation accuracy and the robustness of resisting unknown measurement uncertainty compared to the conventional method.
TL;DR: Simulation and experiment results prove that the proposed methodology can curb the interferences of both kinematic and observation modelling errors on state estimation, leading to improved positioning accuracy for vehicle positioning via tightly-coupled GNSS/INS integration.
Abstract: Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integration is of importance to vehicle positioning. However, this integration technology has difficulty in achieving optimal positioning solutions for the dynamic systems involving strong nonlinearity and systematic modelling error. This paper proposes a new methodology to address the problem of tightly-coupled GNSS/INS integration. This methodology rigorously derives a novel adaptive CKF (Cubature Kalman Filter) with fading memory for kinematic modelling error and a new robust CKF with emerging memory for observation modelling error, using the concept of Mahalanobis distance without involving artificial empiricism. Based on this, a new CKF with both adaptability and robustness is further developed by fusing the results of the standard CKF, adaptive CKF and robust CKF via the principle of interacting multiple model (IMM). Simulation and experiment results together with comparison analysis prove that the proposed methodology can curb the interferences of both kinematic and observation modelling errors on state estimation, leading to improved positioning accuracy for vehicle positioning via tightly-coupled GNSS/INS integration.
TL;DR: A cubature Kalman filter with a multi-rate residual correction algorithm is proposed to fuse INS and PC navigation outputs with different sampling frequencies and shows superior accuracy of the proposed method for the yaw angle measurement fusion even when the PC is temporarily unusable.
TL;DR: The multipath in a cellular-based navigation framework is characterized and it is shown that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.
Abstract: A receiver autonomous integrity monitoring (RAIM) framework for ground vehicle navigation using ambient cellular signals of opportunity (SOPs) and an inertial measurement unit (IMU) is developed. The proposed framework accounts for two types of errors that compromise the integrity of the navigation solution: (i) multipath and (ii) unmodeled biases in the cellular pseudorange measurements due to line-of-sight (LOS) signal blockage and high signal attenuation. This paper, first, characterizes the multipath in a cellular-based navigation framework. Next, a fault detection and exclusion technique for a cellular-based navigation framework is developed. Simulation and experimental results with real long-term evolution (LTE) signals are presented evaluating the efficacy of the proposed RAIM-based fault detection and exclusion technique on a ground vehicle navigating in a deep urban environment in the absence of global navigation satellite system (GNSS) signals. The experimental results on a ground vehicle traversing 825 m in an urban environment show that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.
TL;DR: In this article, a student's t-based Kalman filter with adaptiveness and robustness is derived for the proposed SINS/USBL tightly integration strategy, the adaptiveness can be obtained by estimating the unknown measurement noise statistics using variational Bayesian (VB) approximation, and the robustness can be achieved by dealing with the measurement outliers based on the Student's t distribution.
Abstract: In order to suppress the impact of some error factors of the existing strap-down inertial navigation system and ultra-short base line (SINS/USBL) position matching loosely integration approach on positioning accuracy, a tightly integration strategy is creatively proposed and designed relying on the derived state error equation and measurement equation, the relative measurement information are directly used as the observation information. The error factors such as attitude error and installation error are considered in the filtering model to avoid the influences on the matching position, and the filter algorithm can be also effectively designed according to the sensor parameters of USBL, which can improve the integration positioning accuracy. Meanwhile, in order to address the decreased positioning performance caused by the measurement uncertainty, a Student's t-based Kalman filter with adaptiveness and robustness is reorganized and derived for the proposed SINS/USBL tightly integration strategy, the adaptiveness can be obtained by estimating the unknown measurement noise statistics using variational Bayesian (VB) approximation, and the robustness can be achieved by dealing with the measurement outliers based on the Student's t distribution. Finally, the feasibility and the superiority of the proposed strategy are evidenced both through the simulations and the field tests in the river.
TL;DR: A vehicle-kinematic-model-based sideslip angle estimation method by fusing the information from an inertial measurement unit (IMU) and global navigation satellite system (GNSS) with aligning the heading from the GNSS.
TL;DR: A feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE) and the method of using gravity to stabilize inertial measurement units data to make the network more robust is proposed.
Abstract: The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.
TL;DR: In this article, the attitude transformation matrix is divided into two parts through introducing the initial inertially fixed navigation frame as inertial frame, and the attitude changes of the navigation frame corresponding to the defined inertial body frame can be exactly calculated with known velocity and position provided by GNSS.
Abstract: The task of strapdown inertial navigation system (SINS) initial alignment is to calculate the attitude transformation matrix from body frame to navigation frame. In this paper, such attitude transformation matrix is divided into two parts through introducing the initial inertially fixed navigation frame as inertial frame. The attitude changes of the navigation frame corresponding to the defined inertial frame can be exactly calculated with known velocity and position provided by GNSS. The attitude from body frame to the defined inertial frame is estimated based on the SINS mechanization in inertial frame. The attitude, velocity and position in inertial frame are formulated together as element of the group of double direct spatial this http URL is proven that the group state model in inertial frame satisfies a particular "group affine" property and the corresponding error model satisfies a "log linear" autonomous differential equation on the Lie algebra. Based on such striking property, the attitude from body frame to the defined inertial frame can be estimated based on the linear error model with even extreme large misalignments. Two different error state vectors are extracted based on right and left matrix multiplications and the detailed linear state space models are derived based on the right and left errors for SINS mechanization in inertial frame. With the derived linear state space models, the explicit initial alignment procedures have been presented. Extensive simulation and field tests indicate that the initial alignment based on the left error model can perform quite well within a wide range of initial attitude errors, although the used filter is still a type of linear Kalman filter. This method is promising in practical products abandoning the traditional coarse alignment stage.
TL;DR: Results of the simulation and experiment demonstrate that the initial error is converged at the beginning stage of navigation process, and the interference from external uncertainty inputs to the integrated navigation system are suppressed effectively with the proposed adaptive H-infinite kalman filter algorithm.
Abstract: Aiming at the problem that the navigation performances of unmanned underwater vehicle (UUV) may be affected by inaccurate prior navigation information and external environmental interference, which may make the accuracy and reliability of strapdown inertial navigation system (SINS) and global position system (GPS) integrated navigation results worse, positioning divergent and system even invalid, an adaptive H-infinite kalman filtering algorithm based on multiple fading factors (MAHKF) is proposed in this paper. Firstly, the time-varying adaptive fading factor is used to modify the filter parameters on-line to make the initial error of navigation filter converge quickly. Secondly, the H-infinite kalman filter of the SINS/GPS system is built on combining the advantages of robust control, which improved the system robustness under extreme external environment. Further, the adaptive threshold γ of the H-infinite kalman filter is introduced to make the filter adaptive to the environment change. Results of the simulation and experiment demonstrate that the initial error is converged at the beginning stage of navigation process, and the interference from external uncertainty inputs to the integrated navigation system are suppressed effectively with the proposed algorithm. Compared with the conventional kalman filter algorithm (KF), the position errors in three directions of the UUV are reduced by 66.57%,67.98% and 64.51% respectively with the proposed MAHKF.
TL;DR: The results show that the proposed tightly-coupled UWB/INS integration method can realize the better positioning performance than that of the conventional EKF in Line-of-Sight (LOS) and Non-Line- of-S sight (NLOS) combined indoor environments.
TL;DR: In this article, a new Kalman filter-based initial alignment method is proposed to weaken the effects of IMU biases, DVL lever arm and installation misalignment angles between IMU and DVL, a closed-loop scheme is presented to simultaneous estimate and compensate these parameter errors and the body attitude matrix based on a linear state-space model.
Abstract: The inertial measurement unit (IMU) biases, DVL lever arm and installation misalignment angles between IMU and doppler velocity log (DVL) have a great influence on in-motion initial alignment for DVL-aided low-cost strap-down inertial navigation system (SINS). A new Kalman filter-based initial alignment method is proposed in this paper. To weaken the effects of IMU biases, DVL lever arm and installation misalignment angles between IMU and DVL, a closed-loop scheme is presented to simultaneous estimate and compensate these parameter errors and the body attitude matrix based on a linear state-space model, which improves the accuracy of vector observations. The constant matrix from initial body frame to initial navigation frame can be determined based on the vector observations by Davenport's q-method. Simulation results illustrate that, for the DVL-aided low-cost SINS, the alignment performance of the proposed initial alignment method is better than that of the compared initial alignment methods.
TL;DR: It is suggested that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP), and full-scale experiments are conducted on board a DP vessel operating off the Norwegian coast.
Abstract: In this article, we suggest that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP). We conduct full-scale experiments with MEMS inertial sensors on board a DP vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity, and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave, and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.
22 Mar 2021
TL;DR: In this article, the authors examined the position precision of purely inertial navigation using an array of redundant, low-cost MEMS sensors and found that gyroscope angle random walk and bias instability are dominant and therefore can be used to estimate navigation performance.
Abstract: This paper examines the position precision of purely inertial navigation using an array of redundant, low-cost MEMS sensors. A carefully designed IMU is used to perform navigation experiments and to analyze the benefits of a sensor array over a single sensor in practice. As our experimental results show, navigation can be improved significantly by calibrating the IMU device regarding scale factors, offsets and cross-axis sensitivity. By comparing predicted navigation error and experimental results it is shown that gyroscope angle random walk and bias instability are dominant and therefore can be used to estimate naviaation performance. The latter improves roughly by a factor of $\sqrt{14}$ when using an array of 14 devices instead of a single one. A Kalman Filter with motion constraints minimizes the error when estimating positions.
TL;DR: Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.
Abstract: The introduction of autonomous vehicles can potentially lead to enhanced situational awareness and safety for road transport. However, the performance required for autonomous operations places stringent requirements on the vehicle navigation systems design. Relevant performance measures include not only the accuracy of the system but also its ability to detect sensor faults within a specified Time-to-Alert (TTA) and without generating an excessive number of false alarms. A new integrated navigation system architecture is proposed which utilizes Global Navigation Satellite Systems (GNSS), low-cost Inertial Navigation Systems (INS), visual odometry and Vehicle Dynamic Models (VDM). The system design is based on various navigation modes, each with independent failure mechanisms and fault-detection capabilities. A two-step data fusion approach is adopted to optimize the system accuracy and integrity performance. This includes a Knowledge-Based Module (KBM) performing a detailed sensor integrity analysis followed by a conventional Extended Kalman Filer (EKF). CAV navigation integrity requirement (i.e., alert limits and time-to-alert) are considered in the KBM where fault detection probabilities are calculated for each mode and translated to protection levels. A simulation case study is executed to verify the performance of each navigation mode in the presence of faults affecting the individual navigation sensors. Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.
TL;DR: In this paper, an improved particle filter-based matching algorithm with a gravity sample vector is proposed, where the range of particle filter and the probability model of the actual location are not specified in the algorithm.
Abstract: Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness.
30 May 2021
TL;DR: In this paper, the authors proposed a novel method for performing inertial aided navigation by using deep neural net-works (DNNs), which takes gyroscope and accelerometer readings as input and regresses for integrated IMU poses (i.e., position and orientation).
Abstract: In this work, we propose a novel method for performing inertial aided navigation, by using deep neural net-works (DNNs). To date, most DNN inertial navigation methods focus on the task of inertial odometry, by taking gyroscope and accelerometer readings as input and regressing for integrated IMU poses (i.e., position and orientation). While this design has been successfully applied on a number of applications, it is not of theoretical performance guarantee unless patterned motion is involved. This inevitably leads to significantly reduced accuracy and robustness in certain use cases. To solve this problem, we design a framework to compute observable IMU integration terms using DNNs, followed by the numerical pose integration and sensor fusion to achieve the performance gain. Specifically, we perform detailed analysis on the motion terms in IMU kinematic equations, propose a dedicated network design, loss functions, and training strategies for the IMU data processing, and conduct extensive experiments. The results show that our method is generally applicable and outperforms both traditional and DNN methods by wide margins.
TL;DR: In this paper, an improved robust robust Kalman filter (KF) algorithm is proposed to handle the measurement noise variance of each DVL beam individually, which can avoid the loss of normal measurement information and thus improve estimation accuracy.
Abstract: Aiming at the problem of outliers in Doppler velocity log (DVL) beam measurements, for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) tightly coupled system, an improved robust Kalman filter (KF) algorithm is proposed, which can handle the measurement noise variance of each DVL beam individually. First, the multidimensional measurement equation is decomposed into several 1-D measurement equations, i.e., different DVL beam measurements are assumed to be obtained by different sensors. Second, the statistical similarity measure (SSM) is introduced to quantify the similarity between two random vectors. And a new cost function based on SSM is constructed for the dynamic system containing one state equation and multiple measurement equations. To optimize this cost function, the iterative procedures to update the posterior state is derived based on the Gauss-Newton iteration algorithm. Both the simulation and lake trial illustrate the superiority and effectiveness of the proposed algorithm. It is shown that in the case of partial DVL beams with outliers, the proposed algorithm can avoid the loss of normal measurement information and thus improve estimation accuracy.
TL;DR: The alignment results demonstrate that the proposed in-motion alignment method for the SINS/DVL integrated navigation system has similar accuracy with the current popular method in which the measurements of DVL are not corrupted by the outliers.
Abstract: The initial alignment process is of vital importance to the strapdown inertial navigation system (SINS), and SINS is consisting of the inertial sensors. In this article, the aiding velocity of Doppler velocity log (DVL) is used to implement the in-motion alignment process. However, the measurements of DVL are easy to corrupt by the outliers, and the outliers will degrade the performance of the initial alignment process. Thus, a robust in-motion alignment method for the SINS/DVL integrated navigation system is proposed in this article. First, the vector construction with the measurements of SINS and DVL is introduced. Then, the discrete form of the vector by considering different sampling rates between SINS and DVL is derived in detail. By analyzing the relationship between observed and reference vectors, the magnitude matching method is devised for outliers detecting. Meanwhile, Huber’s robust theory is used to calculate the weighted value with the magnitude matching method. Using the weighted value, the filter-QUEST attitude determined method is modified to implement a robust attitude determination. Finally, the simulation and field tests are designed to validate the performance of the proposed method. The alignment results demonstrate that the proposed method has similar accuracy with the current popular method in which the measurements of DVL are not corrupted by the outliers.
TL;DR: This paper investigates the performance of the proposed algorithm on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences, and shows that the Neural Network-based approaches are able to provide up to 89.55 % improvement on the INS displacement estimation and 93.35 % on theINS orientation rate estimation.
Abstract: An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position More so, the error drift is characterised by a pattern dependent on time This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU) In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences The results obtained show that the Neural Network-based approaches are able to provide up to 8955% improvement on the INS displacement estimation and 9335% on the INS orientation rate estimation
TL;DR: A novel noise covariance estimation algorithm is proposed for the GNSS/INS-integrated navigation using multitask learning model, which can simultaneously estimate the process Noise covariance and measurement noise covariances for the KF.
Abstract: In the recent years, the availability of accurate vehicle position becomes more urgent. The global navigation satellite systems/inertial navigation system (GNSS/INS) is the most used integrated navigation scheme for land vehicles, which utilizes the Kalman filter (KF) to optimally fuse GNSS measurement and INS prediction for accurate and robust localization. However, the uncertainty of the process noise covariance and the measurement noise covariance has a significant impact on Kalman filtering performance. Traditional KF-based integrated navigation methods configure the process noise covariance and measurement noise covariance with predefined constants, which cannot adaptively characterize the various and dynamic environments, and obtain accurate and continuous positioning results under complex environments. To obtain accurate and robust localization results under various complex and dynamic environments, in this article, we propose a novel noise covariance estimation algorithm for the GNSS/INS-integrated navigation using multitask learning model, which can simultaneously estimate the process noise covariance and measurement noise covariance for the KF. The predicted multiplication factors are used to dynamically scale process noise covariance matrix and measurement noise covariance matrix respectively according to the inputs of raw inertial measurement. Extensive experiments are conducted on our collected practical road data set under three typical complex urban scenarios, such as, avenues, viaducts, and tunnels. Experimental results demonstrate that compared with the traditional KF-based integrated navigation algorithm with predefined fixed settings, our proposed method reduces 77.13% positioning error.