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Showing papers on "Inertial navigation system published in 2019"


Journal ArticleDOI
TL;DR: Current research trends in the field of AUVs and future research directions are presented and localization and navigation techniques such as inertial navigation to simultaneous localization and mapping being used in current AUVs are discussed in detail.

250 citations


Book
02 Dec 2019
TL;DR: In this paper, the author compiles everything a student or experienced developmental engineer needs to know about supporting technologies associated with the rapidly evolving field of robotics, including dead reckoning, odometry sensors, Doppler and inertial navigation, tactile and tactile sensors.
Abstract: The author compiles everything a student or experienced developmental engineer needs to know about the supporting technologies associated with the rapidly evolving field of robotics.From the table of contents: Design Considerations * Dead Reckoning * Odometry Sensors * Doppler and Inertial Navigation * Typical Mobility Configurations * Tactile and

232 citations


Proceedings ArticleDOI
20 May 2019
TL;DR: Visual-inertial navigation systems (VINS) have become ubiquitous in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors as discussed by the authors.
Abstract: As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work – which is unfortunately missing in the literature while being greatly demanded by researchers and engineers – in the hope to accelerate the VINS research and beyond in our society as a whole.

141 citations


Posted Content
TL;DR: This paper surveys thoroughly the research efforts taken in visual-inertial navigation research and strives to provide a concise but complete review of the related work in the hope to accelerate the VINS research and beyond in the authors' society as a whole.
Abstract: As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -- which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -- in the hope to accelerate the VINS research and beyond in our society as a whole.

136 citations


Journal ArticleDOI
Chong Shen1, Yu Zhang1, Jun Tang1, Huiliang Cao1, Jun Liu1 
TL;DR: The dual optimization process using different estimators provides better error compensation results than a single optimization method, which demonstrates that the proposed solution leads to the better performance of a MEMS-based INS/GPS navigation system.

119 citations


Journal ArticleDOI
TL;DR: Results indicate that the proposed HIMM-aided INS/DVL integration solution shows superiority than the traditional IMM method when the observation noises and outliers exist and can successfully bridge the DVL's bottom-track outages.
Abstract: To enhance the performance of the navigation system under the complex underwater environment, a hybrid interacting multiple model (HIMM) aided inertial navigation system (INS)/Doppler velocity logs (DVL) integration solution is proposed. First, to employ the acoustic Doppler current profiler mode of DVL, a novel INS/DVL mechanism is constructed where the water current velocity is estimated in real time and both bottom-track and water-track velocity measurements of DVL are involved in the observation vector. Meanwhile, to deal with the outliers and observation noises in the DVL's measurements, a HIMM algorithm is proposed to adaptively select the proper models to describe the changing environment. Simulations and field test are conducted to evaluate the effectiveness of the proposed algorithm, where the interacting multiple model (IMM) algorithm is employed for comparison. The results indicate that the proposed HIMM-aided INS/DVL integration solution shows superiority than the traditional IMM method when the observation noises and outliers exist. Meanwhile, the proposed integration scheme can successfully bridge the DVL's bottom-track outages.

86 citations


Journal ArticleDOI
TL;DR: Experimental results show that the proposed sensor fusion method can effectively reduce the heading drift of inertial navigation and make the captured dual-foot motion closer to its actual process.
Abstract: Foot-mounted inertial navigation is an important issue in areas such as pedestrian localization, gait analysis, and sport training. However, low-cost inertial sensors suffer from several errors that make the navigation results less convincing. In this paper, a multi-sensor approach with one sensor on each foot is presented to reduce the system heading drift. Through dual-gait analysis, gait parameters between two feet are employed to make the non-collocated and uncoupled subsystems be related to each other. A step length estimator based on an inverted pendulum model is developed to derive a relative position vector between the two foot-mounted sensors rather than a distance scalar. A Kalman-type filter with one time update and two measurement updates is developed to fuse the velocity and position observations at foot and person levels, respectively. Experiments were conducted by four healthy subjects, and experimental results show that the proposed sensor fusion method can effectively reduce the heading drift of inertial navigation and make the captured dual-foot motion closer to its actual process.

81 citations


Journal ArticleDOI
TL;DR: The results show that the proposed GNSS/INS/LiDAR-SLAM integrated navigation system can effectively improve the navigation positioning accuracy and can significantly mitigate the navigation error, especially for cases of GNSS signal attenuation or interruption.
Abstract: A Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Light Detection and Ranging (LiDAR)-Simultaneous Localization and Mapping (SLAM) integrated navigation system based on graph optimization is proposed and implemented in this paper. The navigation results are obtained by the information fusion of the GNSS position, Inertial Measurement Unit (IMU) preintegration result and the relative pose from the 3D probability map matching with graph optimizing. The sliding window method was adopted to ensure that the computational load of the graph optimization does not increase with time. Land vehicle tests were conducted, and the results show that the proposed GNSS/INS/LiDAR-SLAM integrated navigation system can effectively improve the navigation positioning accuracy compared to GNSS/INS and other current GNSS/INS/LiDAR methods. During the simulation of one-minute periods of GNSS outages, compared to the GNSS/INS integrated navigation system, the root mean square (RMS) of the position errors in the North and East directions of the proposed navigation system are reduced by approximately 82.2% and 79.6%, respectively, and the position error in the vertical direction and attitude errors are equivalent. Compared to the benchmark method of GNSS/INS/LiDAR-Google Cartographer, the RMS of the position errors in the North, East and vertical directions decrease by approximately 66.2%, 63.1% and 75.1%, respectively, and the RMS of the roll, pitch and yaw errors are reduced by approximately 89.5%, 92.9% and 88.5%, respectively. Furthermore, the relative position error during the GNSS outage periods is reduced to 0.26% of the travel distance for the proposed method. Therefore, the GNSS/INS/LiDAR-SLAM integrated navigation system proposed in this paper can effectively fuse the information of GNSS, IMU and LiDAR and can significantly mitigate the navigation error, especially for cases of GNSS signal attenuation or interruption.

78 citations


Book
28 Nov 2019
TL;DR: The text looks at experiments on the launch of space missions and the different mathematical techniques used to measure the movement of a variable-mass vehicle and how solar radiation affects pressure on satellite attitude control.
Abstract: Guidance and Control focuses on space guidance models and behavior control techniques needed in space missions. Divided into eight parts with 30 chapters, the book contains the literature of authors who have conducted extensive research on factors affecting space missions. The concerns include ascent from Earth to an orbit requiring navigation as well as descent to Earth or the moon; the system aspects of inertial navigation; and developments in modern control theory and attitude control. The text looks at experiments on the launch of space missions and the different mathematical techniques used to measure the movement of a variable-mass vehicle. The selection also notes the processes and techniques involved in keeping satellites in compatible orbits; the influence of calculus of perturbations as applied to lunar mission analysis; and tracking of space vehicles through satellites and radar. The book also presents guidance systems for soft lunar landing and the longitudinal control of a lifting vehicle entering a planetary atmosphere. Other concerns include the application of sideband folding techniques to navigation satellite system; Damping an inertial navigation system; and application of multiple inertial system in navigation. The text ends by highlighting the use of gyroscopes in space navigation and infrared navigation sensors in space vehicles and how solar radiation affects pressure on satellite attitude control. The book is valuable for readers interested in studying the factors involved in space missions.

63 citations


Journal ArticleDOI
TL;DR: Simulations and real experiments show that the approach ensures state-of-the-art VIN performance while maintaining a lean processing time, and outperforms appearance-based feature selection in terms of localization errors.
Abstract: We study a visual-inertial navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of VIN? Our approach has four key ingredients. First, it is task-driven , in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation , since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement , since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees : we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate VIN while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.

63 citations


Journal ArticleDOI
TL;DR: This paper proposes a new analytical preintegration theory for graph-based sensor fusion with an inertial measurement unit (IMU) and a camera and develops both direct and indirect visual–inertial navigation systems (VINSs) that leverage this theory.
Abstract: In this paper, we propose a new analytical preintegration theory for graph-based sensor fusion with an inertial measurement unit (IMU) and a camera (or other aiding sensors). Rather than using disc...

Journal ArticleDOI
18 Feb 2019-Sensors
TL;DR: TapeLine is proposed, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride and walking-distance using the low-cost inertial-sensor embedded in a smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments.
Abstract: Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

Journal ArticleDOI
TL;DR: A low-cost INS and UWB integrated pedestrian tracking system is proposed using only one UWB anchor node at an unknown location, minimizing infrastructure cost and setup and achieves up to 85% reduction in average positioning error when compared with an INS-only solution.
Abstract: An inertial navigation system (INS) using kinematic sensors is able to provide accurate pedestrian tracking over a short distance. Information fusion of the INS with wireless technology has been commonly employed to develop more robust and accurate tracking systems over longer distances. An ultra-wideband (UWB)-based system offers further improvement and achieves positioning error in order of centimetres. Existing INS and UWB integrated systems require at least three anchors at known locations for positioning in the covered area. The deployment cost of such systems is high, since UWB devices are more expensive than other commonly used wireless nodes. The need to obtain the exact locations of all anchor nodes is also contributed to the additional cost in system deployment. In this paper, a low-cost INS and UWB integrated pedestrian tracking system is proposed using only one UWB anchor node at an unknown location, minimizing infrastructure cost and setup. The anchor location is estimated at the initial stage of tracking with the aid of an INS tracking algorithm. Afterward, the UWB ranging measurements are fused with INS data to provide robust and accurate tracking performance. The system is evaluated with pedestrian tracking experiments over long distances and achieves up to 85.75 % reduction in average positioning error when compared with an INS-only solution.

Journal ArticleDOI
TL;DR: To improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm.
Abstract: Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

Journal ArticleDOI
TL;DR: A novel equality constraint method based on pedestrian footstep mode is proposed to fuse the information of two systems in the absence of any additional ranging equipment and demonstrates that the proposed algorithm improves the performances of both two systems.
Abstract: Foot-mounted inertial navigation systems are suitable as the core pedestrian navigation system in GNSS denied environment for some applications (e.g., soldier and first responder positioning). However, to date the exiting zero velocity update-based algorithm still possesses some drawbacks including systematic heading drift. A beneficial strategy is to fuse the information of two foot-mounted systems to achieve more robust and reliable performance. In this paper, a novel equality constraint method based on pedestrian footstep mode is proposed to fuse the information of two systems in the absence of any additional ranging equipment. Real field experiments demonstrate that the proposed algorithm improves the performances of both two systems.

Journal ArticleDOI
13 Dec 2019-Science
TL;DR: In this paper, the authors discuss enabling technologies relevant to a set of key functional building blocks of an atom chip-based compact inertial sensor with cold guided atoms, including accurate and reproducible positioning of atoms to initiate a measurement cycle, coherent momentum transfer to the atom wave packets, suppression of propagation-induced decoherence due to potential roughness, on-chip detection, and vacuum dynamics because of its impact on sensor stability.
Abstract: This work reviews the topic of rotation sensing with compact cold atom interferometers. A representative set of compact free-falling cold atom gyroscopes is considered because, in different respects, they establish a rotation-measurement reference for cold guided-atom technologies. This review first discusses enabling technologies relevant to a set of key functional building blocks of an atom chip-based compact inertial sensor with cold guided atoms. These functionalities concern the accurate and reproducible positioning of atoms to initiate a measurement cycle, the coherent momentum transfer to the atom wave packets, the suppression of propagation-induced decoherence due to potential roughness, on-chip detection, and vacuum dynamics because of its impact on sensor stability, which is due to the measurement dead time. Based on the existing enabling technologies, the design of an atom chip gyroscope with guided atoms is formalized using a design case that treats design elements such as guiding, fabrication, scale factor, rotation-rate sensitivity, spectral response, important noise sources, and sensor stability.

Journal ArticleDOI
TL;DR: Evaluation based on experimental data shows the significant improvement by the proposed semi-tightly coupled integration scheme with low-cost INS/GNSS and LiDAR, which is able to achieve 1–2 m’ accuracy in terms of positioning and mapping.

Journal ArticleDOI
TL;DR: Results illustrate the proposed fusion methodology to provide pseudo GPS position information can significantly improve the navigation accuracy during GPS outages and the model is simpler.
Abstract: The performance of an inertial navigation system (INS) and global positioning system (GPS) integrated navigation system is reduced during GPS outages. To bridge GPS outages, a fusion methodology to provide pseudo GPS position information is proposed. The methodology consists of two parts, empirical mode decomposition threshold filtering (EMDTF) and a long short-term memory (LSTM) neural network. The EMDTF eliminates the noise in inertial sensors and provides more accurate data for subsequent calculations. The LSTM uses the current specific forces and angular rates to predict the pseudo GPS position during GPS outages. To evaluate the effectiveness of the proposed methodology, numerical simulations and real field tests are employed. Compared with the traditional artificial neural networks, the results illustrate the proposed methodology can significantly improve the navigation accuracy during GPS outages and the model is simpler.

Proceedings ArticleDOI
04 Nov 2019
TL;DR: This is the first paper which combines sophisticated deep learning techniques with state-of-the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial Navigation techniques.
Abstract: This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final distance error of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-of the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.

Journal ArticleDOI
21 Jan 2019-Sensors
TL;DR: A PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems using a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest.
Abstract: Demand for indoor navigation systems has been rapidly increasing with regard to location-based services. As a cost-effective choice, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) systems have been developed for years because they do not require external devices to be installed in the environment. In this paper, we propose a PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems. Since the IMU is mounted on a part of the upper body, the framework of the zero-velocity update cannot be applied because there are no periodical moments of zero velocity. Therefore, we propose a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest. In addition, we integrated the idea of an efficient map-matching algorithm based on particle filtering into our system to improve positioning and heading accuracy. Since our system was designed for 3D navigation, which can estimate position in a multifloor building, we used a barometer to update pedestrian altitude, and the components of our map are designed to explicitly represent building-floor information. With our complete PDR system, we were awarded second place in 10 teams for the IPIN 2018 Competition Track 2, achieving a mean error of 5.2 m after the 800 m walking event.

Journal ArticleDOI
17 Jan 2019
TL;DR: In this letter, in-depth observability analysis is performed for both spatial and temporal calibration parameters of an aided inertial navigation system (INS) with global and/or local sensing modalities and identifies four degenerate motion primitives that harm the calibration accuracy and should be avoided in reality whenever possible.
Abstract: In this letter, we perform in-depth observability analysis for both spatial and temporal calibration parameters of an aided inertial navigation system (INS) with global and/or local sensing modalities. In particular, we analytically show that both spatial and temporal calibration parameters are observable if the sensor platform undergoes random motion. More importantly, we identify four degenerate motion primitives that harm the calibration accuracy and thus should be avoided in reality whenever possible. Interestingly, we also prove that these degenerate motions would still hold even in the case where global pose measurements are available. Leveraging a particular multi-state constrained Kalman filter based vision-aided INS with online spatial and temporal calibration, we perform extensively both Monte-Carlo simulations and real-world experiments with the identified degenerate motions to validate our analysis.

Journal ArticleDOI
TL;DR: A hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization with the design of a novel measurement model that exploits all of the measurements and information available within a sliding window.
Abstract: In this paper, we present a hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization. The MSCKF is a well-known visual inertial odometry (VIO) method that performs the fusion between an inertial measurement unit (IMU) and the image measurements within a sliding window. The MSCKF computes the re-projection errors from the camera measurements and the states in the sliding window. During this process, the structure-only estimation is performed without exploiting the full information over the window, like the relative interstate motion constraints and their uncertainties. The key contribution of this paper is combination of the filtering and non-linear optimization method for VIO, and the design of a novel measurement model that exploits all of the measurements and information available within the sliding window. The local visual-inertial optimization is performed using pre-integrated IMU measurements and camera measurements. It infers the probabilistically optimal relative pose constraints. These local optimal constraints are used to estimate the global states under the MSCKF framework. The proposed local-optimal-multi-state constraint Kalman filter is validated using a simulation data set, as well as publicly available real-world data sets generated from real-world urban driving experiments.

Journal ArticleDOI
07 Aug 2019
TL;DR: In this paper, a factor graph optimization method for state estimation is presented, which tightly fuses and smooths inertial navigation, leg odometry and visual odometry to reduce position drift during dynamic motions such as trotting.
Abstract: Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position drift during dynamic motions such as trotting. To this end, we present a factor graph optimization method for state estimation which tightly fuses and smooths inertial navigation, leg odometry and visual odometry. The effectiveness of the approach is demonstrated using the ANYmal quadruped robot navigating in a realistic outdoor industrial environment. This experiment included trotting, walking, crossing obstacles and ascending a staircase. The proposed approach decreased the relative position error by up to 55% and absolute position error by 76% compared to kinematic-inertial odometry.

Journal ArticleDOI
15 May 2019
TL;DR: In this article, a Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented, which extends existing zerovelocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior information about the two hypotheses (the sensors being stationary or in motion) to be incorporated into the test.
Abstract: A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior information about the two hypotheses—the sensors being stationary or in motion—to be incorporated into the test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider a hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.

Proceedings ArticleDOI
19 May 2019
TL;DR: The security guarantees of INS-aided GPS tracking and navigation for road transportation systems are evaluated and countermeasures that limit an attacker's ability are proposed, without the need for any hardware modifications are proposed.
Abstract: Location information is critical to a wide variety of navigation and tracking applications. GPS, today's de-facto outdoor localization system has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination and monitored by an INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We develop and evaluate algorithms that achieve this goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also design, build and demonstrate that the magnetometer can be actively spoofed using a combination of carefully controlled coils. To experimentally demonstrate and evaluate the feasibility of the attack in real-world, we implement a first real-time integrated GPS/INS spoofer that accounts for traffic fluidity, congestion, lights, and dynamically generates corresponding spoofing signals. Furthermore, we evaluate our attack on ten different cities using driving traces and publicly available city plans. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the actual destination without being detected. We also show that it is possible for the adversary to reach almost 60--80% of possible points within the target region in some cities. Such results are only a lower-bound, as an adversary can adjust our parameters to spend more resources (e.g., time) on the target source/destination than we did for our performance evaluations of thousands of paths. We propose countermeasures that limit an attacker's ability, without the need for any hardware modifications. Our system can be used as the foundation for countering such attacks, both detecting and recommending paths that are difficult to spoof.

Journal ArticleDOI
TL;DR: The test results show that the proposed model can efficiently predict the increment of position and compensate the INS errors accumulation during GPS outage and the advantage of new model on positioning accuracy becomes more obvious when the GPS observations are unavailable for a long time.
Abstract: In order to improve the performance of the global positioning system (GPS) and inertial navigation system (INS) integrated system during GPS outages, a novel fusion algorithm based on back propagation neural network (BPNN) is proposed. A new model is built which relates the INS velocity, inertial measurement unit (IMU) outputs and the duration of GPS outages to the GPS position increment. Performance of the proposed method has been experimentally evaluated in a land vehicle navigation test. The test results show: (1) the proposed model can efficiently predict the increment of position and compensate the INS errors accumulation during GPS outage; (2) the advantage of new model on positioning accuracy becomes more obvious when the GPS observations are unavailable for a long time; (3) utilizing the current and past 2-step information as the input of BPNN model can effectively balance the computation burden and accuracy.

Journal ArticleDOI
TL;DR: Detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix.
Abstract: This paper presents detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective. Both the system error and measurement models of strapdown inertial navigation system (SINS)/Odometer (OD) tightly-coupled navigation are derived based on the ST-EKF. Both theoretical analysis and test results indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix. The excellent covariance-consistency of the ST-EKF makes it not necessary to remove gyro and accelerometer bias errors from the initial alignment Kalman filter states. This phenomenon can ensure the subsequent integrated navigation performance. Single-position ground SINS alignment experiments by using a navigation grade inertial measurement unit (IMU) showed that the estimated yaw angle from the conventional 15-state EKF slowly diverged over time. Furthermore, this phenomenon became more distinct when the initial yaw angle error was larger. In contrast, the estimated yaw angle from the proposed 15-state ST-EKF was significantly more stable and less degraded by large initial yaw angle errors. Long- distance land-vehicle SINS/OD integrated navigation tests also exhibited the ST-EKF's higher positioning and heading accuracy than the EKF.

Journal ArticleDOI
TL;DR: The proposed IO-RUKF can not only correct the UKF sensitivity to measurement errors, but also avoids the loss of accuracy for state estimation in the absence of measurement errors.
Abstract: Due to the high maneuverability of a hypersonic vehicle, the measurements for tightly coupled INS/GNSS (inertial navigation system/global navigation satellite system) integration system inevitably involve errors. The typical measurement errors include outliers in pseudorange observations and non-Gaussian noise distribution. This paper focuses on the nonlinear state estimation problem in hypersonic vehicle navigation. It presents a new innovation orthogonality-based robust unscented Kalman filter (IO-RUKF) to resist the disturbance of measurement errors on navigation performance. This IO-RUKF detects measurement errors by use of the hypothesis test theory. Subsequently, it introduces a defined robust factor to inflate the covariance of predicted measurement and further rescale the Kalman gain such that the measurements in error are less weighted to ensure the filtering robustness against measurement errors. The proposed IO-RUKF can not only correct the UKF sensitivity to measurement errors, but also avoids the loss of accuracy for state estimation in the absence of measurement errors. The efficacy and superiority of the proposed IO-RUKF have been verified through simulations and comparison analysis.

Journal ArticleDOI
TL;DR: A deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neuralnetwork for noise modeling of a micromechanics system gyroscope, and results supported a positive conclusion on the performance of designed method.
Abstract: Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

Proceedings ArticleDOI
20 May 2019
TL;DR: This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking anddepth sensor for plane extraction.
Abstract: This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.