Showing papers on "Inertial navigation system published in 2020"
TL;DR: The basic performance of BDS-3 is described and some methods to improve the positioning, navigation and timing (PNT) service are suggested and the comprehensive and resilient PNT infrastructures are proposed for the future seamless PNT services.
Abstract: The core performance elements of global navigation satellite system include availability, continuity, integrity and accuracy, all of which are particularly important for the developing BeiDou global navigation satellite system (BDS-3). This paper describes the basic performance of BDS-3 and suggests some methods to improve the positioning, navigation and timing (PNT) service. The precision of the BDS-3 post-processing orbit can reach centimeter level, the average satellite clock offset uncertainty of 18 medium circular orbit satellites is 1.55 ns and the average signal-in-space ranging error is approximately 0.474 m. The future possible improvements for the BeiDou navigation system are also discussed. It is suggested to increase the orbital inclination of the inclined geostationary orbit (IGSO) satellites to improve the PNT service in the Arctic region. The IGSO satellite can perform part of the geostationary orbit (GEO) satellite’s functions to solve the southern occlusion problem of the GEO satellite service in the northern hemisphere (namely the “south wall effect”). The space-borne inertial navigation system could be used to realize continuous orbit determination during satellite maneuver. In addition, high-accuracy space-borne hydrogen clock or cesium clock can be used to maintain the time system in the autonomous navigation mode, and stability of spatial datum. Furthermore, the ionospheric delay correction model of BDS-3 for all signals should be unified to avoid user confusion and improve positioning accuracy. Finally, to overcome the vulnerability of satellite navigation system, the comprehensive and resilient PNT infrastructures are proposed for the future seamless PNT services.
279 citations
TL;DR: An integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalmanfilter (UKF) to improve the robustness and accuracy and two random motion approximation model algorithms are proposed and evaluated in the real environment.
Abstract: The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.
150 citations
13 Mar 2020
TL;DR: This paper proposes a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU) using the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter.
Abstract: In this paper, we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision.
140 citations
01 May 2020
TL;DR: A new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions, and novel neural inertial navigation architectures, making significant improvements for challenging motion cases are presented.
Abstract: This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of horizontal positions and heading direction of a moving subject from a sequence of IMU sensor measurements from a phone. In contrast to existing methods, our method can handle varying phone orientations and placements.More concretely, the paper presents 1) a new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions; 2) novel neural inertial navigation architectures, making significant improvements for challenging motion cases; and 3) qualitative and quantitative evaluations of the competing methods over three inertial navigation benchmarks. We share the code and data to promote further research. (http://ronin.cs.sfu.ca).
136 citations
TL;DR: Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors, and this review covers a brief overview on the inertialerror modeling techniques used to enhance the performance of low-cost sensors.
Abstract: Inertial navigation represents a unique method of navigation, in which there is no dependency on external sources of information. As opposed to other position fixing navigation techniques, inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform. Hence, inertial navigation systems are not prone to jamming, or spoofing. Inertial navigation systems have developed vastly, from their occurrence in the 1940s up to date. The accuracy of the inertial sensors has improved over time, making inertial sensors sufficient in terms of size, weight, cost, and accuracy for navigation and guidance applications. Within the past few years, inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena, from which the dynamic forces exerted on a moving body could be computed accurately. Besides, the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system, which were mechanically complicated, to computationally demanding strap-down inertial navigation systems. Optical sensory technologies have provided highly accurate inertial sensors, at smaller sizes. Besides, the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost, and offer extremely low size, weight and power consumption, making them suitable for a wide range of day-to-day navigation applications. Recently, advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology, cold-atom technology, and the re-introduction of fluid-based inertial sensors. On another note, inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors, which would be a huge leap. Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors. In this review, the inertial sensor technologies are covered extensively, along the future trends in the inertial sensors’ technologies. Besides, this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.
112 citations
TL;DR: A novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor is proposed, in which beam measurements are used without transforming them to 3-D velocity, which can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment.
Abstract: In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.
75 citations
TL;DR: A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed to enable localization in an urban environment where GNSS signals could be unusable or unreliable.
Abstract: A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed. This framework aims to enable localization in an urban environment where GNSS signals could be unusable or unreliable. The proposed framework employs an extended Kalman filter (EKF) to fuse pseudorange observables extracted from cellular SOPs, IMU measurements, and GNSS-derived position estimates (when available). The EKF is coupled with a map-matching approach. The framework assumes the positions of the cellular towers to be known, and it estimates the vehicle's states (position, velocity, orientation, and IMU biases) along with the difference between the vehicle-mounted receiver clock error states (bias and drift) and each cellular SOP clock error state. The proposed framework is evaluated experimentally on a ground vehicle navigating in a deep urban area with a limited sky view. Results show a position root-mean-square error (RMSE) of 2.8 m across a 1,380-m trajectory when GNSS signals are available and an RMSE of 3.12 m across the same trajectory when GNSS signals are unavailable for 330 m. Moreover, compared to localization with a loosely coupled GNSS?IMU integrated system, a 22% reduction in the localization error is obtained whenever GNSS signals are available, and an 81% reduction in the localization error is obtained whenever GNSS signals are unavailable.
70 citations
TL;DR: The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal to enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.
Abstract: Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.
66 citations
TL;DR: A robust Student’s t-based Kalman filter for strap-down inertial navigation system and ultra-short base line (SINS/USBL) integration system is proposed to suppress the measurement uncertainty induced by the acoustic outliers.
Abstract: In order to satisfy the requirements of the placement, the operation, and the high-precision navigation and positioning for the underwater vehicles and the underwater operational platform, a SINS/USBL integration navigation strategy is proposed. This paper presents a robust Student’s t-based Kalman filter for strap-down inertial navigation system and ultra-short base line (SINS/USBL) integration system, which is proposed to suppress the measurement uncertainty induced by the acoustic outliers. Firstly, a SINS/USBL integration prototype system is designed and presented, which is constructed by an inertial measurement unit (IMU) and an USBL acoustic array in an inverted configuration, and they can be entirely designed and developed in-house. Furthermore, an improved robust Student’s t-based Kalman filter with the degree of freedom (dof) parameter is proposed to better address the acoustic outliers in the measured range and directions information, the heavy-tailed measurement noise induced by the acoustic outliers can be modelled as a Student’s t distribution, the posterior probability density functions (PDFs) of the state variable, the auxiliary random variable and the dof parameter are updated as Gaussian, Gamma, and Gamma prior PDF, respectively, and the corresponding statistics and the state vector are jointly inferred using the variational Bayesian (VB) approach. Finally, based on the state error equations and the derived measurement equation of SINS/USBL integration navigation system, the mathematical simulation test and the field trial are performed to demonstrate the feasibility and the superiority of the proposed SINS/USBL integration approach.
63 citations
TL;DR: In this paper, the authors present the Oxford Inertial Odometry Data Set (OxIOD), a first-of-its-kind public data set for deep learning-based inertial navigation research with fine-grained ground truth on all sequences.
Abstract: Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet of Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this article, we present and release the Oxford Inertial Odometry Data Set (OxIOD), a first-of-its-kind public data set for deep-learning-based inertial navigation research with fine-grained ground truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our data set and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
63 citations
TL;DR: An ultra wideband/inertial navigation system (UWB/INS) integrated pedestrian navigation algorithm is proposed, and UWB-based joint state estimation particle filtering is used for position calculation and INS-based zero-speed update (ZUPT) algorithms are used for navigation information solution.
Abstract: Goal: Accurate robust ultra wideband (UWB) pedestrian indoor positioning becomes a challenging task when faced with robust indoor environments such as non-line of sight (NLOS) due to refraction of signals, multipath effect, etc. When the UWB system is faced with such a robust environment, its positioning accuracy is difficult to reach the centimeter level and the reliability of the system operation needs to be improved. In order to solve such problems, an ultra wideband/inertial navigation system (UWB/INS) integrated pedestrian navigation algorithm is proposed. On the one hand, UWB-based joint state estimation particle filtering is used for position calculation, and on the other hand, INS-based zero-speed update (ZUPT) algorithm is used for navigation information solution. Under the framework of the INS error equation, the navigation information fusion of the two systems is carried out. In the simple pedestrian environment, the average positioning accuracy of the UWB/INS algorithm is 53.8% higher than that of the UWB algorithm, 40% higher than the INS algorithm and 31% higher than Original UWB/INS algorithm. Under the robust pedestrian indoor positioning, the average positioning accuracy of the UWB/INS algorithm is 39.7% higher than that of the UWB algorithm, 37.5% higher than the INS algorithm and 53% higher than Original UWB/INS algorithm. The results of two sets of pedestrian indoor positioning experiments demonstrate the effectiveness of our approach.
TL;DR: The localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes are addressed and the generalizability of the OEC modules are demonstrated.
Abstract: While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.
TL;DR: Two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation by incorporating a motion classifier that adaptively updates the detector’s threshold parameter and a long short-term memory recurrent neural network are presented.
Abstract: We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector’s threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.
TL;DR: The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term.
Abstract: In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.
TL;DR: An interacting multiple model and unscented Kalman filter (IMM-UKF) aided strapdown inertial navigation system (SINS)/Ultra-Short Base Line (USBL) calibration solution is proposed for the common calibration procedure, where only one transponder is employed without pre-locating.
Abstract: To improve the efficiency of the calibration process and enhance the adaptability of the calibration method, an interacting multiple model and unscented Kalman filter (IMM-UKF) aided strapdown inertial navigation system (SINS)/Ultra-Short Base Line (USBL) calibration solution is proposed for the common calibration procedure, where only one transponder is employed without pre-locating. Firstly, construct the SINS/USBL calibration mechanism, where the transponder's position, level-arm and misalignment angle are estimated simultaneously utilizing the slant range, inclination angles of USBL and depth information from depthometer. Second, to mitigate the decreasing calibration accuracy caused by the single filtering parameter under the complex underwater environment, an IMM-UKF algorithm is presented to support the proposed calibration mechanism. Simulation results indicate that the proposed mechanism earns faster convergence rate and better calibration results than other solutions. Besides, the proposed solution can maintain its robustness when the observation quality changes.
10 Feb 2020
TL;DR: This letter introduces the fusion of UnscentedKalman filter and linear Kalman filter for joint estimation of the navigation states and unknown sea currents in multiple Autonomous Underwater Vehicles in unknown currents.
Abstract: This letter is on cooperative localization and sea currents estimation using multiple Autonomous Underwater Vehicles (AUVs). Due to sea currents, dead reckoning localization based on inertial navigation sensors results in accumulation of localization error. To reduce the error accumulation, this letter proposes navigation algorithm to allow multiple AUVs to simultaneously estimate their navigation states and unknown sea currents. This letter introduces the fusion of Unscented Kalman filter and linear Kalman filter for joint estimation of the navigation states and unknown sea currents. Suppose that only one AUV, called the leader, is equipped with Doppler Velocity Logs (DVL) and Ultra Short Base Line (USBL) sensors. To improve the localization of multiple AUVs, the leader measures both the bearing and the range of its nearby AUV periodically. As far as we know, this manuscript is unique in considering cooperative localization of multiple AUVs in unknown currents. The effectiveness of this cooperative localization is verified using MATLAB simulations.
TL;DR: New strategies to cope with the integration of an inertial navigation system, a global navigation satellite system, and light detection and ranging (LIDAR) to achieve simultaneous localization and mapping (INS/GNSS/LiDAR SLAM) especially in GNSS challenging environments where GNSS signals are blocked or contaminated with reflected signals is proposed.
Abstract: In the near future, multi-sensor fusion will be the core component to navigate the autonomous driving platforms. This paper proposes new strategies to cope with the integration of an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LIDAR) to achieve simultaneous localization and mapping (INS/GNSS/LiDAR SLAM) especially in GNSS challenging environments where GNSS signals are blocked or contaminated with reflected signals. The proposed strategies implement a high level of integration with various information received from multiple sensors to collectively compensate for the specific drawbacks of those sensors included in the integrated system. The first strategy is to solve the divergence and drift problems of SLAM using the initial pose information from INS and the proposed refreshing process using an INS/GNSS integrated system. In addition, an updated mechanization is designed to qualify those received measurements based on cross validation of separate types of data. This mechanization is to ensure all measurements are reliable for the Extended Kalman Filter (EKF) update process. Moreover, the SLAM-derived information plays a major role to recognize the vehicle movement which assists the system to accurately apply those appropriate vehicle motion constraint models. The preliminary results presented in this study illustrate that proposed algorithm performs superior than the traditional INS/GNSS integration scheme and provides absolute navigation accuracy of 2 meters and 0.6% of distance traveled in GNSS-denied as well as 1.2 meters in GNSS-hostile environments, respectively.
TL;DR: A novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband which is able to obtain high-precision position and orientation estimates at low cost and the system complexity is reduced.
Abstract: In the field of indoor pedestrian navigation (IPN), the orientation information of a pedestrian is often obtained by means of strap-down inertial navigation system (SINS). To deal with the problem of divergence in SINS based orientation estimates, additional orientation sensors, such as a camera, are needed to provide external orientation observations, resulting in increased cost and complexity of system. Although a low-cost magnetometer (or compass) can be used, it is significantly affected by geomagnetic disturbances indoors. Besides, the magnetometer can only give the heading observation which is insufficient to correct orientation errors in all three directions. In this paper, we propose a novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband. The biggest advantage of this method is able to obtain high-precision position and orientation estimates at low cost. In addition, in the proposed method, the data fusion is implemented by a quaternion Kalman filter which does not involve any complex linearization and hence the system complexity is reduced. Experimental results show that a decimeter level position accuracy is achieved and the orientation drifts can be limited to 0.066 radians in indoor environments.
TL;DR: A bioinspired polarization-based attitude and heading reference system (PAHRS) aided by inertial sensors is presented and the results illustrate that the PAHRS is able to achieve satisfactory performance without the aid by GNSS and magnetic compass system.
Abstract: How to self-determine the attitude and heading of users is a challenging issue in unknown, global navigation satellite system (GNSS) denied, or magnetic disturbance environments. In this article, a bioinspired polarization-based attitude and heading reference system (PAHRS) aided by inertial sensors is presented. As compared with the existing studies, a polarization compass, which consists of three photoelectric-based polarization sensors is designed to cope with the three-dimensional polarization heading determination and distinguish issue. Hence, the real heading orientation can be determined without GNSS correction. To address the triaxis attitude and heading determination issue of PAHRS, the system measurement model tightly coupled the attitude and heading accumulate errors of inertial navigation system. Finally, the experimental platform is built and the results illustrate that the PAHRS is able to achieve satisfactory performance without the aid by GNSS and magnetic compass system.
TL;DR: A novel position estimation system based on learning to the prediction model to address the above challenges and it is observed that the proposed Kalman filter with learning module performed better than the traditionalKalman filter algorithm in terms of root mean square error metric.
Abstract: Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.
TL;DR: Experimental results show that the accuracy of the proposed integrated navigation and positioning algorithm can be improved and the system remains stable even when the error occurs.
Abstract: Real-time positioning feature is becoming an essential part in a variety of military and civilian applications. In particular, the cooperation of multisensors helps to improve navigation accuracy and the universality of the system. In this article, we propose a novel robust fault-tolerant federated filter based on strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), celestial navigation system (CNS), and doppler velocity log (DVL). In the proposed algorithm, the position information of the GNSS, the velocity information of the DVL, and the attitude information of the CNS are introduced as measurement information to correct the divergence error of SINS. The federated filter is exploited for its flexibility and capability in fault toleration. To amend fault and recover information in local filters, a simplified state Chi-square test (SSCST) is utilized as the fault detection, isolation, and recovery procedure. Meanwhile, a new adaptive information sharing factor algorithm based on the results of SSCST and the residuals between the actual observations and the predicted observations is designed, which can adaptively reflect the performance of each local filter. Experimental results show that the accuracy of the proposed integrated navigation and positioning algorithm can be improved and the system remains stable even when the error occurs.
TL;DR: A robust GNSS/MEMS-strapdown inertial navigation system (SINS), the tightly coupled navigation approach aided by non-holonomic constraint (NHC) is proposed, the tight integration model is adopted to overcome the accuracy degradation caused by changing satellite geometry and frequent GNSS outages in urban areas.
Abstract: Special approaches are required to integrate low-cost MEMS-based inertial navigation system (MEMS-INS) with global navigation satellite system (GNSS). This article proposes a robust GNSS/MEMS-strapdown inertial navigation system (SINS), the tightly coupled navigation approach aided by non-holonomic constraint (NHC). The tight integration model is adopted to overcome the accuracy degradation caused by changing satellite geometry and frequent GNSS outages in urban areas. The innovation-based test statistic is devised to enhance the filtering with the resistance to GNSS outliers. To further improve the stand-alone accuracy during relatively long GNSS outages, the virtual measurement is also introduced using the NHC condition that land vehicle does not slip and always remain in contact with the ground. The field test results with continuous GNSS aiding indicate that the proposed algorithm can improve overall accuracy in position, velocity, and attitude by about 16, 43, and 19% with immunity from GNSS outliers, compared with the traditional loosely coupled (LC) method. Even during 60-s GNSS outages, the proposed algorithm can effectively compensate MEMS-SINS errors, accomplishing the overall accuracy improvements by about 46% (position), 35% (velocity), and 15% (attitude), compared with the traditional LC counterpart.
31 May 2020
TL;DR: The approach to extended poses combined with log-linearity property allows revisiting the theory of preintegration on manifolds and reaching a further theoretic level in this field.
Abstract: To fuse information from inertial measurement units (IMU) with other sensors one needs an accurate model for IMU error propagation in terms of position, velocity and orientation, a triplet we call extended pose. In this paper we leverage a nontrivial result, namely log-linearity of inertial navigation equations based on the recently introduced Lie group SE 2 (3), to transpose the recent methodology of Barfoot and Furgale for associating uncertainty with poses (position, orientation) of SE(3) when using noisy wheel speeds, to the case of extended poses (position, velocity, orientation) of SE 2 (3) when using noisy IMUs. Besides, our approach to extended poses combined with log-linearity property allows revisiting the theory of preintegration on manifolds and reaching a further theoretic level in this field. We show exact preintegration formulas that account for rotating Earth, that is, centrifugal force and Coriolis effect, may be derived as a byproduct.
TL;DR: Two structure from motion strategies are provided, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.
Abstract: Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.
TL;DR: A multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM).
Abstract: Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.
TL;DR: Simulation and experimental results illustrate that the proposed GPS-aided coarse alignment method can achieve better accuracy than existing state-of-the-art coarse alignment methods for MEMS-based SINS.
Abstract: In order to improve the computational efficiency and alignment accuracy of a microelectromechanical system (MEMS)-based strap-down inertial navigation system (SINS), this article proposes a high-accuracy global positioning system (GPS)-aided coarse alignment method. The attitude matrix between current and initial body frames and the unknown gyro bias, accelerometer bias, and lever arm are jointly estimated based on the proposed closed-loop approach, where the attitude error and unknown parameters are jointly inferred based on the derived linear state-space model using the Kalman filter. Simulation and experimental results illustrate that the proposed GPS-aided coarse alignment method can achieve better accuracy than existing state-of-the-art coarse alignment methods for MEMS-based SINS.
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TL;DR: The Oxford Inertial Odometry Data Set is presented and released, a first-of-its-kind public data set for deep-learning-based inertial navigation research with fine-grained ground truth on all sequences, and a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data is proposed.
Abstract: Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
TL;DR: An adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach, is proposed, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes.
Abstract: Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.
TL;DR: This paper proposes a new approach for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage that significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked.
Abstract: Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.
TL;DR: A novel INS and Ultra-wideband (UWB) fusion approach, which complements INS only with ranging measurements obtained from UWB anchors placed at known location, is proposed and incorporated in a Particle Filter fusion algorithm, which reduces errors of the UWB measurements and enhances positioning accuracy.
Abstract: Fusion techniques are employed in pedestrian tracking to achieve more accurate and robust tracking systems. A common approach is to fuse Inertial Navigation System (INS), worn by a pedestrian, with a radio-based system to complement each other and mitigate their shortcomings. Despite the increased accuracy achieved in the state-of-the-art approaches, the deployment complexity and cost of these tracking systems remain a major bottleneck. In this paper, a novel INS and Ultra-wideband (UWB) fusion approach, which complements INS only with ranging measurements obtained from UWB anchors placed at known location, is proposed. An adaptive UWB ranging uncertainty model is proposed and incorporated in a Particle Filter fusion algorithm, which reduces errors of the UWB measurements and enhances positioning accuracy. The proposed approach achieves significant reduction of the deployment complexity and cost compared to other approaches that have comparable tracking performance. The pedestrian tracking system is implemented using the built-in inertial measurement unit of a smartphone and DecaWave TREK1000 UWB development kit. Two practical long-distance pedestrian tracking experiments are conducted to demonstrate the accuracy and robustness of the proposed approach, which reduces mean position error up to 73.23 % when compared to INS only tracking results.