Showing papers on "Inertial navigation system published in 2022"

The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications

Abstract: Resilient navigation in Global Navigation Satellite System (GNSS)-degraded and -denied environments is becoming more and more required for many applications. It can typically be based on multi-sensor data fusion that relies on alternative technologies to GNSS. In this work, we studied the potential of a low earth orbit (LEO) satellite communication system for a high-dynamic application, when it is integrated with an inertial measurement unit (IMU) and magnetometers. We derived the influence of the main error sources that affect the LEO space vehicle (SV) Doppler-based navigation on both positioning and attitude estimations. This allowed us to determine the best, intermediate and worst cases of navigation performances. We show that while the positioning error is large due to large orbit errors or high SV clock drifts, it becomes competitive with that of an inertial navigation system (INS) based on a better quality IMU if precise satellite orbits are available. On the other hand, the attitude estimation tolerates large orbit errors and high SV clock drifts. The obtained results suggest that LEO SV signals, used as signals of opportunity for navigation, are an attractive alternative in GNSS-denied environments for high dynamic vehicles.

Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation

Abstract: The low cost inertial navigation system (INS) suffers from bias and measurement noise, which would result in poor navigation accuracy during global positioning system (GPS) outages. Aiming to bridge GPS outages duration and enhance the navigation performance, a deep learning network architecture named GPS/INS neural network (GI-NN) is proposed in this paper to assist the INS. The GI-NN combines a convolutional neural network and a gated recurrent unit neural network to extract spatial features from inertial measurement unit (IMU) signals and track their temporal characteristics. The relationship among the attitude, specific force, angular rate and the GPS position increment is modelled, while the current and previous IMU data are used to estimate the dynamics of the vehicle by GI-NN. Numerical simulations, real field tests and public data tests are performed to evaluate the effectiveness of the proposed algorithm. Compared with the traditional machine learning algorithms, the results illustrate that the proposed method can provide more accurate and reliable navigation solutions in GPS denied environments.

Advances toward fieldable atom interferometers

Abstract: ABSTRACT We review the field of atom interferometer inertial sensors. We begin by reviewing the path integral formulation of atom interferometers and then specialize the treatment to light-pulse atom interferometers and, in particular, gravimeters and gyroscopes. The bulk of the article focuses on the most common type of atom interferometer – the light-pulse interferometer, where the atom optics are composed of light pulses. Our article mainly focuses on a review of advances that aid in the practical implementation of atom interferometers toward gravimetry and inertial navigation. To that end, we develop a navigation model that aids in the connection of parameters and performance of atom interferometers to actual quantities of interest to the navigation community. Practical considerations of atomic inertial sensors, including dynamic range, bandwidth, dead time, and cross-coupling effects are discussed, before we review the field of accelerometer and gyroscope atom interferometers. Finally, we review advances in trapped-atom interferometers. Graphical abstract

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Abstract: A review of various calibration techniques of MEMS inertial sensors is presented in this paper. MEMS inertial sensors are subject to various sources of error, so it is essential to correct these errors through calibration techniques to improve the accuracy and reliability of these sensors. In this paper, we first briefly describe the main characteristics of MEMS inertial sensors and then discuss some common error sources and the establishment of error models. A systematic review of calibration methods for inertial sensors, including gyroscopes and accelerometers, is conducted. We summarize the calibration schemes into two general categories: autonomous and nonautonomous calibration. A comprehensive overview of the latest progress made in MEMS inertial sensor calibration technology is presented, and the current state of the art and development prospects of MEMS inertial sensor calibration are analyzed with the aim of providing a reference for the future development of calibration technology.

A Data-Driven Inertial Navigation/Bluetooth Fusion Algorithm for Indoor Localization

Abstract: The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

A Computationally Efficient Outlier-Robust Cubature Kalman Filter for Underwater Gravity Matching Navigation

Abstract: Gravity-aided navigation is one of key techniques for the navigation of underwater vehicles. Cubature Kalman filter (CKF)-based matching algorithm improves the positioning accuracy of traditional Sandia inertial terrain aided navigation method. However, the gravity sensor may suffer from outlier interferences due to complex and changeable underwater environments, which degrades the performance of CKF-based matching algorithm remarkably. To address this problem, in this article, a novel computationally efficient outlier-robust CKF-based matching algorithm is proposed for an underwater gravity-aided navigation system with outlier-contaminated measurements. The convergence analysis and the stability discussions are given to show the effectiveness of the proposed algorithm, and the discussion on computational complexity illustrates the good real-time performance of the proposed algorithm. Simulation and experimental results demonstrate the advantages of the proposed matching algorithm as compared with existing state-of-the-art matching algorithms.

An Enhanced LiDAR Inertial Localization and Mapping System for Unmanned Ground Vehicles

Abstract: The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for unmanned ground vehicles, which is appropriate for versatile search and rescue applications. Compared with existing LiDAR-based localization and mapping system such as LOAM, we have two major contributions: the first is the improvement of the robustness of particle swarm filter-based LiDAR SLAM, while the second is the loop closure methods developed for global optimization to improve the localization accuracy of the whole system. We demonstrate by experiments that the accuracy and robustness of the LiDAR SLAM system are both improved. Finally, we have done systematic experimental tests at the Hong Kong science park as well as other indoor or outdoor real complicated testing circumstances, which demonstrates the effectiveness and efficiency of our approach. It is demonstrated that our system has high accuracy, robustness, as well as efficiency. Our system is of great importance to the localization and mapping of the unmanned ground vehicle in an unknown environment.

Strapdown Inertial Navigation System Initial Alignment Based on Group of Double Direct Spatial Isometries

Abstract: The task of strapdown inertial navigation system (SINS) initial alignment is to calculate the attitude transformation matrix from body frame to navigation frame. In this paper, such attitude transformation matrix is divided into two parts through introducing the initial inertially fixed navigation frame as inertial frame. The attitude changes of the navigation frame corresponding to the defined inertial frame can be exactly calculated with known velocity and position provided by GNSS. The attitude from body frame to the defined inertial frame is estimated based on the SINS mechanization in inertial frame. The attitude, velocity and position in inertial frame are formulated together as element of the group of double direct spatial isometries (). It is proven that the group state model in inertial frame satisfies a particular “group affine” property and the corresponding error model satisfies a “log-linear” autonomous differential equation on the Lie algebra. Based on such striking property, the attitude from body frame to the defined inertial frame can be estimated based on the linear error model with even extreme large misalignments. Two different error state vectors are extracted based on right and left matrix multiplications and the detailed linear state-space models are derived based on the right and left errors for SINS mechanization in inertial frame. With the derived linear state-space models, the explicit initial alignment procedures have been presented. Extensive simulation and field tests indicate that the initial alignment based on the left error model can perform quite well within a wide range of initial attitude errors, although the used filter is still a type of linear Kalman filter. This method is promising in practical products abandoning the traditional coarse alignment stage.

UAV Vision Aided INS/Odometer Integration for Land Vehicle Autonomous Navigation

Abstract: Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles.

PINDOC: Pedestrian Indoor Navigation System Integrating Deterministic, Opportunistic, and Cooperative Functionalities

Abstract: In this paper, we present a Pedestrian Indoor Navigation system integrating Deterministic, Opportunistic, and Cooperative functionalities (PINDOC) for multi-agent navigation. The deterministic module produces a navigation solution for each agent by utilizing a Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation System (INS) augmented with an altimeter and foot-to-foot range measurements. The opportunistic module augments the deterministic solutions with pseudorange measurements extracted from cellular Long-Term Evolution (LTE) signals. The navigation accuracy of each individual agent is further enhanced by cooperative localization using Ultra-WideBand (UWB)-based inter-agent range measurements. We developed a dedicated multi-sensor pedestrian navigation hardware testbed and conducted an experiment to evaluate the navigation performance of different combinations of components comprising PINDOC: ZUPT-aided INS, altimeter, foot-to-foot ranging, LTE signals, and inter-agent ranging. The experiment involved three agents, with one agent traversing a trajectory of 600 meters in 14 minutes, during which, the other two agents remained stationary. The traversed trajectory included terrains of flat surfaces, stairs, ramps, and elevators. The PINDOC system showed high accuracy, achieving a position Root-Mean-Squared Error (RMSE), maximum error, and final error of 0.93 m, 2.23 m, and 1.28 m over the 600-meter trajectory.

Online Relative Positioning of Autonomous Vehicles Using Signals of Opportunity

Abstract: To fully realize the potential of autonomous aerial vehicles, a reliable, robust, and accurate navigation system is required. Current technologies composing such a system include stand-alone and augmented global navigation satellite systems (GNSS), as well as visual, inertial, and signals-of-opportunity (SOPs) approaches. However, as it is well understood that no single alternative can meet the requirements for full autonomy, a combination of technologies is required to address alternative application scenarios. In this work, an online technique for relative positioning of autonomous unmanned aerial vehicles (UAVs) is proposed that capitalizes on SOPs in conjunction with inertial measurements for localization and navigation when GNSS signals are not readily available. The proposed system uses the signals’ characteristics over a large part of the frequency spectrum, in conjunction with an optimal learning (OL) technique, to adaptively choose a sequence of frequencies that provide location estimates in real-time. Subsequently, inertial measurements, which are provided by an onboard unit, are employed to improve on the tracking accuracy. Field experiments, using an autonomous unmanned aerial vehicle, demonstrate the effectiveness of the proposed solution under various parameter settings.

A Machine Learning Approach for an Improved Inertial Navigation System Solution

Abstract: The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

Inertial-Based Integration With Transformed INS Mechanization in Earth Frame

Abstract: This article proposes to use a newly derived transformed inertial navigation system (INS) mechanization to fuse INS with other complementary navigation systems. Through formulating the attitude, velocity, and position as one group state of a group of double direct spatial isometries $\mathbb{S}{\mathbb{E}_2}(3)$ , the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent. In order to make use of the transformed INS mechanization in inertial-based integration, both the right and left vector error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial-based integration. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the $\mathbb{S}{\mathbb{E}_2}(3)$ based error state covariance, and feedback correction corresponding to the error state definitions. Extensive Monte Carlo simulations and land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications. Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate.

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

Abstract: Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31 × to 134 × with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ± 0 . 1 𝑚 and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models. Razor IMU board logs inertial sensor data from an agricultural robot onto an SD card, with developed neural-inertial model running in real-time on the board after training. Motion capture (MoCap) markers are attached to the robot to log ground truth position on the track using high-resolution infrared cameras with respect to the ground plane.

A SINS Aided Correct Method for USBL Range Based on Maximum Correntropy Criterion Adaptive Filter

Abstract: The integrated navigation system for strap-down inertial navigation system and ultrashort baseline (SINS/USBL) is one of the main approaches to realize the high-precision navigation and positioning of autonomous underwater vehicle (AUV). The relatively low frequency with seconds and the noise caused by the complex environment for USBL measurements have an obviously influence on the accuracy of positioning system. To solve the above problems, a correct method for USBL range measurement and a robust filter based on maximum correntropy criterion (MCC) are proposed in this article. First, the influence of motion effect on the range calculation results in the process of USBL transmission and reception is analyzed. Then, a simple correction method based on short-term high-precision characteristics of SINS is proposed. The short-term high-precision attitude, velocity, and position information of SINS in the process of USBL transmission and reception is used to correct the range error caused by motion effect. Finally, an adaptive filter based on MCC and dynamic model error adaptive factor is proposed to suppress the influence of outliers on positioning accuracy. The simulation and Yangzi River experiments are conducted to evaluate the feasibility of the proposed algorithm. The results shows that the proposed range correction method has a good correction effect on range error between sending and receiving epoch, and the proposed algorithm has better ability to suppress outliers.

A Robust In-Motion Optimization-Based Alignment for SINS/GPS Integration

Abstract: Initial alignment is of vital importance to the inertial navigation system, and the in-motion initial alignment is a critical stage for the moving vehicles. In this paper, an in-motion alignment method for SINS/GPS integration is investigated. Using the velocity, which is obtained from GPS, the observation and the reference vectors are constructed. Different from previous methods, a robust attitude determination method is devised to finish the in-motion alignment process when there are outliers contained in the GPS outputs. To implement the robust attitude determination method, the error of the magnitudes of the observation and reference vectors is calculated, and this error is used to detect the outliers. However, the bias of the inertial sensors, the expectation and variance of the calculated error of the magnitude is time-varying, so that the detected accuracy of the outliers degrades. To address this defect, the new normalized error of the magnitude is constructed by a robust parameter identification method, then the expectation and variance of the new normalized error are constant. Using the new normalized error, the initial attitude is obtained by the robust optimization-based alignment (ROBA) method. The simulation and field tests are designed to validate the performance of the proposed method, the alignment results show that the proposed method can detect the outliers accurately, and it gets the similar alignment accuracy with the current popular method when there are no outliers in the auxiliary velocity of GPS.

An Effective Integrity Monitoring Scheme for GNSS/INS/Vision Integration Based on Error State EKF Model

Abstract: It is well-known that the Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) /Vision integration has been increasingly used for safety-critical applications like self-driving cars. However, the performance of the GNSS/INS/Vision integration degrades due to unknown faults. Therefore, it is essential to develop integrity monitoring algorithms for the integrated navigation system. A new protection level (PL) formula to ensure the integrity of the state estimation of the GNSS/INS/Vision integration is proposed, which can be calculated by the relation between the filter estimation error and faults. An integrity risk allocation tree is developed for each sensor fault hypothesis including the nominal hypothesis, and the specific PL is given in the case of GNSS, INS and visual measurement faults. In addition, the performance of the proposed PL is comprehensively evaluated. A field vehicular test was conducted to evaluate the performance of the integrity monitoring algorithm for the GNSS/INS/Vision integration. The results demonstrate that the proposed PL for the GNSS/INS/Vision integration can fit the position error against six different fault modes.

An INS/GNSS fusion architecture in GNSS denied environment using gated recurrent unit

Abstract: One of the most used Position, Navigation and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit line-of-sight and reduce satellite availability to receivers. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as transportation of organs which are time-sensitive. Therefore, higher accuracy for position and velocity information is required. This paper investigates the use of Gated Recurrent Units (GRU) as a suitable technique that can memorize previous information in conjunction with the inputs (consisting of attitude, change in attitude, and change in velocity) to reduce position and velocity error when GNSS is not available. The fusion approach is developed and tested using Spirent’s SimGEN GSS7000 hardware simulator which simulates GNSS signals and Spirent’s SimSENSOR software that simulates accelerometer and gyroscope stochastic and deterministic errors. GNSS outage is varied between 1 and 20 seconds randomly to affect predicted position and velocity. The data is collected and used to train the GRU to predict the position and velocity error measured by the Inertial Measurement Unit (IMU). From the performance evaluation, a 60% reduction in Root Mean Squared Error (RMSE) is observed compared to Recurrent Neural Networks (RNN). Comparing 95th percentile with Inertial Navigation System (INS), RNN, and GRU, an 80% reduction is observed between INS and RNN. Furthermore, a 35% drop in the 95th percentile is observed between RNN and GRU.