Integrated Navigation Scheme Research Articles

GNSS/INS integrated navigation algorithm framework based on cascade vector tracking algorithm for USV

The safety of Unmanned Surface Vehicle during the autonomous phase of navigation relies heavily on the onboard navigation equipment. However, in the face of changing and diverse navigation environments, global satellite navigation systems utilizing radio communication often face signal occlusions. This significantly reduces the positioning accuracy of navigation systems. To ensure the navigation accuracy of vehicles in various harsh environments, a Global Navigation Satellite Systems and Inertial Navigation System (GNSS/INS) integrated navigation system structure based on cascaded vector-tracking loop is proposed in this paper. The integrated navigation scheme not only improved the positioning accuracy of the navigation system but also effectively improved the robustness of the system. Compared with the traditional scalar-tracking loop and vector-tracking loop methods, it exhibits better performance. The experimental results show that the proposed method can reduce the horizontal position positioning error by 73.7% compared with the traditional vector-tracking loop method in the case of signal occlusion. Simultaneously, it can continuously output reliable and high-precision positioning information in the case of serious signal occlusion. The integrated navigation scheme proposed in this paper can be used as a research direction for the development of Unmanned Surface Vehicle navigation applications and provides solutions for the development of related navigation systems.

Enhancing TDCP-specified GNSS/INS tightly coupled filter robustness in cascaded for low-cost urban applications

Abstract Integration of global navigation satellite systems (GNSS) with inertial navigation systems (INS) is commonly employed to enhance navigation performance. However, conventional integrated algorithms suffer from low-quality GNSS measurements due to either inaccurate pseudorange or difficulty in ambiguity resolution when utilizing carrier phase (CP) measurements in urban environments. We propose a new time-differenced CP (TDCP-) specified GNSS/INS tightly coupled integration algorithm for low-cost urban applications. The proposed method employs a dual-rate filter scheme, wherein the potential of TDCP observations and INS-derived positions are fully harnessed for state estimation with PR outlier inhibition and CP small cycle slip repair. Field test results in urban environments demonstrate that the integrated navigation scheme proposed is superior to conventional algorithms, with an average improvement of about 13.4%, 19.2%, and 25.5% in position, velocity, and attitude, respectively.

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

The signal blockage and multipath effects of the Global Navigation Satellite System (GNSS) caused by urban canyon scenarios have brought great technical challenges to the positioning and navigation of autonomous vehicles. In this paper, an improved factor graph optimization algorithm enhanced by a resilient noise model is proposed. The measurement noise is resilient and adjusted based on an approximate Gaussian distribution-based estimation. In estimating and adjusting the noise parameters of the measurement model, the error covariance matrix of the multi-sensor fusion positioning system is dynamically optimized to improve the system accuracy. Firstly, according to the approximate Gaussian statistical property of the GNSS/odometer velocity residual sequence, the measured data are divided into an approximate Gaussian fitting region and an approximate Gaussian convergence region. Secondly, the interval is divided according to the measured data, and the corresponding variational Bayesian network and Gaussian mixture model are used to estimate the innovation online. Further, the noise covariance matrix of the adaptive factor graph-based model is dynamically optimized using the estimated noise parameters. Finally, based on low-cost inertial navigation equipment, GNSS, odometer, and vision, the algorithm is implemented and verified using a simulation platform and real-vehicle road test. The experimental results show that in a complex urban road environment, compared with the traditional factor graph fusion localization algorithm, the maximum improvement in accuracy of the proposed algorithm can reach 65.63%, 39.52%, and 42.95% for heading, position, and velocity, respectively.

A SINS/GNSS/2D-LDV integrated navigation scheme for unmanned ground vehicles

In recent years, unmanned autonomous driving technology has attracted increasing attention from people, and become a research hotspot. Currently, the integration of strapdown inertial navigation system (SINS) and global navigation satellite systems (GNSSs) is the most common and effective navigation and positioning scheme for unmanned ground vehicles (UGVs) and unmanned aerial vehicles. However, this integrated system is unable to maintain a reliable positioning solution in challenging environments due to the inherent weakness of GNSS signals and the accumulation of SINS positioning errors over time. To address this issue, this paper proposes an integrated scheme based on an asynchronous Kalman filter for SINS, GNSS and two-dimensional (2D) laser Doppler velocimeter (LDV). In the proposed scheme, the SINS and 2D-LDV are tightly coupled to improve the robustness of the integrated system, and the error parameters between the 2D-LDV and the SINS are calibrated in real time during the validity of the GNSS signal. In addition, the designed asynchronous Kalman filter method evaluates the validity of the GNSS and 2D-LDV measurements in real time based on Mahalanobis distance of innovation vector and statistical property principle. Two groups of long-distance, high-mobility vehicle experiments conducted in challenging environments verify the validity of the proposed scheme. The experimental results show that the proposed SINS/GNSS/2D-LDV integrated navigation scheme has good environmental adaptability and reliability, and can maintain high horizontal and vertical positioning accuracy despite frequent GNSS signal failures, which can meet the needs of UGVs.

A novel INS/USBL/DVL integrated navigation scheme against complex underwater environment

In view of challenges of complex underwater environments with a high occurrence of sensor outliers, traditional Kalman filter (KF)-based localization methods are difficult to achieve precise positioning. To address this issue, a factor graph optimization (FGO)-based tightly-coupled (TC) inertial navigation system (INS)/ultra-short baseline system (USBL)/Doppler velocity log (DVL) scheme and core fusion algorithm are proposed. The FGO method is used to construct a TC INS/USBL/DVL factor graph model, and a weight function is added to adjust the weights of each factor, thereby improving the utilization of underwater sensor information and enhancing the accuracy and reliability of underwater navigation. The feasibility of the proposed scheme is verified through simulations and field tests. The results show that the proposed method outperforms KF-loosely-coupled (KF-LC), KF-TC, and FGO-LC methods, with horizontal position accuracy improvements of 29.2%, 6.6%, and 13.2%, respectively. Moreover, the proposed method exhibits smooth navigation error performance in the presence of abnormal sensor information.

Performance Analysis of Integrated Navigation of New Energy vehicle based on GPS+BDS-3 PPP/INS/ODO in complex Transmission Line Environment

In a complex environment, the single navigation and positioning method has problems of poor accuracy, reliability, and integrity. This paper proposes a PPP/INS/ODO integrated navigation scheme of GPS+BDS-3. Using the GPS+BDS-3 dual system, the non-ionization combined PPP model is derived, the PPP/INS/ODO combined model is further given, and the processing strategy of time asynchrony and lever arm error is analyzed. Combining the odometer speed measurement information and non-integrity constraint information, it improves the positioning performance of integrated navigation to a certain extent. In the state of GPS+BDS-3 non-ionization combination, PPP and PPP/INS/ODO integrated navigation are analyzed respectively using the simulated dynamic, and new energy measured data. Through the experiment, it can be concluded that the positioning accuracy of the combination of GPS and Beidou 3 is higher than that of a single system to some extent. The PPP/INS/ODO integrated system can provide technical support for new energy vehicle navigation in complex environments with RMS better than 1m.

An autonomous smartphone embedded inertial sensors aided vehicular navigation method in satellite-denied areas

An autonomous smartphone embedded inertial sensors aided vehicular navigation method is proposed in this study to solve the low accuracy of the embedded sensors and the indefinite installation angle between a smartphone and a vehicle in a smartphone-based vehicular navigation. First, the nonlinear MEMS-IMU model and nonlinear NHC model are derived, and analysis of the error generated by the traditional model under this case is conducted, proving the necessity of the nonlinear modeling in theory. Second, we establish the nonlinear integrated navigation scheme, the app is also implemented with the scheme on the Android platform. The satellite-denied simulation experiment verifies the accuracy of the proposed method is higher than that of the traditional method. The app performs well in the smartphone-based platform in the actual tunnel and underground parking lot tests.

An INS-aided MASS autonomous navigation algorithm considering virtual motion constraints and the leeway and drift angle

Autonomous navigation is one of the critical enabling technologies in developing MASS or intelligent ships. This study proposes an autonomous navigation algorithm with INS-aided shipborne GNSS. The proposed algorithm constructs the ship's virtual motion attitude by considering the leeway and drift angle, and takes into account the one-dimensional speed constraints in the virtual ship body reference frame. This approach addresses the issue that the conventional INS and shipborne GNSS integrated navigation scheme can utilize the three-dimensional position information only, without considering the measurement update from the one-dimensional speed obtained by shipborne GNSS. The algorithm is verified by actual ship departure and cruising experiments in natural marine environments. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm when the GNSS speed and position information are available. In the scenario when the GNSS position information is temporarily lost, the proposed algorithm can continue to derive relatively reliable navigation parameters for the safe navigation of ships with only one-dimensional speed measurement.

A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering

One of the core issues of mobile measurement is the pose estimation of the carrier. The classic Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) integrated navigation scheme has high positioning accuracy but is vulnerable to Global Navigation Satellite System (GNSS) signal occlusion and multipath effect. Simultaneous Localization and Mapping (SLAM) is not affected by signal occlusion, but there are problems such as error accumulation and scene degradation. The multi-sensor fusion scheme combining the two technologies can effectively expand the scene coverage and improve the positioning accuracy and system robustness. However, the current scheme has some limitations. On the one hand, GNSS plays a less important role in most SLAM systems, only for initialization or as a closed-loop factor and other auxiliary work. On the other hand, in the fusion method, most of the current systems only use filtering or graph optimization, without taking into account the advantages of both. Aiming at pose estimation for mobile carriers, this paper combines the advantages of the global optimization of the factor graph and the local optimization of filtering and proposes a GNSS-IMU-LiDAR Constraint Kalman Filter (abbreviated as GIL-CKF), which has the characteristics of full coverage and effectively improving absolute accuracy and high output frequency. The scheme proposed in this paper consists of three parts. Firstly, an extensible factor map is used to fuse the positioning information from different sources, including GNSS, IMU, LiDAR, and a closed-loop map, to maintain a high-precision SLAM system, and the output is used as Multi-Sensor-Fusion-Odometry (MSFO). Then, the scene is divided according to the GNSS quality factor, and a Scene Optimizer (SO) is designed to filter GNSS pose and MSFO. Finally, the results are input into the Extended Kalman Filter (EKF) together with the original IMU data for further optimization and smoothing. The experimental results show that the integration of high-precision GNSS positioning information with IMU, LiDAR, a closed-loop map, and other information through the factor map can effectively suppress error accumulation and improve the overall accuracy of the SLAM system. The SO based on GNSS indicators can fully retain high-precision GNSS positioning information, effectively play their respective advantages of filtering and graph optimization, and alleviate the conflict between global and local optimization. SO with EKF filtering furthers optimization, can improve the output frequency, and smooth the trajectory. GIL-CKF can effectively improve the accuracy and robustness of pose estimation and has obvious advantages in multi-sensor scene complementarity, partial road section accuracy improvement, and high input frequency.

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

Eccentric Optimization of Multisensor for SLAM-Integrated Navigation

This paper models, first, the eccentric installation of multiple sensors and inertial measurement unit (IMU), both dynamically and physically, based on the Simultaneous Localization and Mapping (SLAM) integrated navigation in the near-ground environment. Furthermore, the relationship between the estimated acceleration of other sensor locations under the eccentric motion and the actual measurement value of accelerometer has been analyzed. Most SLAM integrated navigation fusion algorithms incorporating IMU only accounts the transformation between the sensor coordinate systems, but avoids the Coriolis, tangential, and centrifugal forces caused by the eccentric rotation and the sensor installed at different locations. This problem affects the accuracy of the sensor acceleration estimated at different locations, and thus, affects the accuracy of the SLAM system pre-integration and the overall positioning accuracy of SLAM. Second, the eccentric correction optimization and pre-integration optimization algorithm have been implemented. Finally, a number of simulation experiments have been carried out to verify the optimal effect of the algorithm. The experimental results show that the positioning accuracy of the overall SLAM navigation algorithm can be effectively improved by eccentric correction optimization, and the RMSE of the optimized SLAM navigation algorithms has been reduced in varying degrees in different paths and algorithms. This study will be helpful for optimizing the positioning accuracy of SLAM and IMU integrated navigation scheme in the future.

A Novel INS/USBL Integrated Navigation Scheme via Factor Graph Optimization

Factor graph optimization (FGO) is a well-known approach in the robotics field. Due to multiple iterations and application of large amounts of data, it usually could superior performance than the Kalman Filter (KF) under complex scenarios. However, it is rarely used in the underwater environment, especially for applications based on fiber-optic inertial navigation. Taking the ultra-short baseline system (USBL) as the application background, a novel inertial navigation system (INS)/USBL integrated navigation scheme based on FGO is presented in this paper. The traditional pre-integration factor simplifies the earth rotation and coordinate system, which limits its application in high-precision sensors. This paper proposed an improved pre-integration model using the high precision measurements of fiber optics gyroscope in FGO. Simulation and field tests proved that, with the FGO method, improved accuracy of INS/USBL integration was obtained compared to the traditional KF method. It confirms the potential of FGO in the applications of underwater navigation and positioning.

A SINS/CNS integrated navigation scheme with improved mathematical horizon reference

Strap-down inertial navigation system (SINS) and celestial navigation system (CNS) integrated navigation have been widely applied in ships, aircraft and spacecraft etc. However, in practice, its performance is highly depending on the horizon reference accuracy, which is usually difficult to guarantee. In this work, we propose a SINS/CNS integrated navigation scheme based on a novel mathematical horizon reference (MHR) determination method. More specifically, inertial coordinate system is used in constructing the MHR, where the attitude and position errors could be decoupled and compensated, respectively. Then, a more accurate MHR could be established, thus resulting in better navigation results. Comparative numerical simulations for a maneuvering flight have been conducted, and it is shown the proposed approach has the best converging speed as well as the highest navigation precision compared with SINS and existing MHR-based SINS/CNS schemes, and all-state optimal estimation could be achieved, thus enabling fully autonomous and high-precision navigation.

Autonomous dynamic docking of UAV based on UWB-vision in GPS-denied environment

This paper studies the autonomous docking between an Unmanned Aerial Vehicle (UAV) and a Mobile Platform (MP) based on UWB and vision sensors. To solve this problem, an integrated estimation and control scheme is proposed, which is divided into three phases: hovering, approaching and landing. In the hovering phase, the velocity of the MP and relative position between the MP and UAV are estimated by using geometric tools and Cayley-Menger determinant based on ultra-wideband distance measurements; in the approaching phase, a recursive least squares optimization algorithm with a forgetting factor is proposed, which uses distance, displacement and MP’s velocity to estimate the relative position between the UAV and MP. With the estimated relative position, UAV can approach MP until reaching a distance such that MP is within the field of view of UAV; in the landing phase, the UWB measurement value and visual perception attitude are integrated with the UAV on-board navigation sensor of the UAV to perform the precision landing. Simulation and experiment results verify the effectiveness and feasibility of the proposed integrated navigation scheme.

Residual Attention Network-Based Confidence Estimation Algorithm for Non-Holonomic Constraint in GNSS/INS Integrated Navigation System

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation.

Comparison Study of Leading Rotary Steerable System and Future Development Trend

The rotary steerable system is critical drilling equipment urgently needed in the development of unconventional oil and gas fields in China. It is still a significant but unavailable technology for China’s petroleum industry. With the high demand for rotary steerable system in unconventional oil and gas development, this paper studies RSS tools from major oilfield service companies. Different tools from major oilfield service companies and their technical specifications were studied. Firstly, the development history of the rotary steerable system is introduced, and the tools are then classified into different categories based on control and biasing methodologies. The key technical parameters of the RSS were compared and analyzed. Moreover, a detailed comparison of the 475, 675 and 900 series of tools has been carried out. The current development focuses of rotary steerable tools include high dogleg severity, high temperature and high pressure and integrated navigation scheme with inertial and magnetic methods. Finally, the development trends of the RSS were forecasted. The objective of this paper is to provide an overview and latest development of the rotary steerable system for developer, operation and maintenance population in the drilling industry.

Design and Performance Verification of RSINS/Odometer Fault-Tolerant Integrated Navigation Scheme

Design and Performance Verification of RSINS/Odometer Fault-Tolerant Integrated Navigation Scheme

Recovery and guidance technology of collision network based on multi-source information fusion

To solve the problem of low precision of pose estimation and poor anti-jamming in the recovery process of small Shipborne UAV, a multi-sensor navigation scheme was proposed, based on vision / IMU / GPS fusion. Firstly, an integrated navigation scheme of multi-sensor fusion in collision recovery guidance was proposed. GPS is used for long-distance guidance. Vision / IMU / GPS fusion guidance is used in approach phase. GPS and visual information are fused based on extended Kalman filter to obtaion position information. IMU and visual information are fused based on least square method to obtain the optimal attitude information. According to this method, the optimal pose information is obtained to improve the guidance accuracy and the success rate of net collision recovery.The simulation results show that the guidance deviation is reduced by 30% compared with the unfused data.

Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation

In the recent years, the availability of accurate vehicle position becomes more urgent. The global navigation satellite systems/inertial navigation system (GNSS/INS) is the most used integrated navigation scheme for land vehicles, which utilizes the Kalman filter (KF) to optimally fuse GNSS measurement and INS prediction for accurate and robust localization. However, the uncertainty of the process noise covariance and the measurement noise covariance has a significant impact on Kalman filtering performance. Traditional KF-based integrated navigation methods configure the process noise covariance and measurement noise covariance with predefined constants, which cannot adaptively characterize the various and dynamic environments, and obtain accurate and continuous positioning results under complex environments. To obtain accurate and robust localization results under various complex and dynamic environments, in this article, we propose a novel noise covariance estimation algorithm for the GNSS/INS-integrated navigation using multitask learning model, which can simultaneously estimate the process noise covariance and measurement noise covariance for the KF. The predicted multiplication factors are used to dynamically scale process noise covariance matrix and measurement noise covariance matrix respectively according to the inputs of raw inertial measurement. Extensive experiments are conducted on our collected practical road data set under three typical complex urban scenarios, such as, avenues, viaducts, and tunnels. Experimental results demonstrate that compared with the traditional KF-based integrated navigation algorithm with predefined fixed settings, our proposed method reduces 77.13% positioning error.

Transverse SINS/DVL Integrated Polar Navigation Algorithm Based on Virtual Sphere Model

Due to the principle error caused by the Earth’s sphere model in transverse polar navigation, the ellipsoid model is usually used for transverse transformation. In order to avoid the complex transverse transformation of the ellipsoid model, a virtual sphere model, which can simplify the transformation of the ellipsoid model, is constructed in this paper. With the requirements of high accuracy and long-time navigation for AUV in the polar region, the integrated navigation scheme of SINS/DVL, which is based on the virtual sphere model, is proposed. The proposed method can improve the navigation accuracy and suppress the oscillation error. The error equations of transverse SINS are established based on the virtual sphere model. Then, the transverse SINS/DVL integrated navigation algorithm is derived according to the new error equations. The simulation results show that the navigation accuracy of the proposed method is equivalent with the traditional ellipsoid model method and is better than that of the traditional sphere model method. However, the complexity of the proposed method is simpler than the traditional ellipsoid model method. Moreover, it is verified that the navigation accuracy of SINS/DVL integrated navigation system based on the virtual sphere model meets the requirements of AUV.