Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integration is of importance to vehicle positioning. However, this integration technology has difficulty in achieving optimal positioning solutions for the dynamic systems involving strong nonlinearity and systematic modelling error. This paper proposes a new methodology to address the problem of tightly-coupled GNSS/INS integration. This methodology rigorously derives a novel adaptive CKF (Cubature Kalman Filter) with fading memory for kinematic modelling error and a new robust CKF with emerging memory for observation modelling error, using the concept of Mahalanobis distance without involving artificial empiricism. Based on this, a new CKF with both adaptability and robustness is further developed by fusing the results of the standard CKF, adaptive CKF and robust CKF via the principle of interacting multiple model (IMM). Simulation and experiment results together with comparison analysis prove that the proposed methodology can curb the interferences of both kinematic and observation modelling errors on state estimation, leading to improved positioning accuracy for vehicle positioning via tightly-coupled GNSS/INS integration.

Cubature Kalman Filter With Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration

Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

Doppler Velocity Logs (DVL) is an important information source for underwater vehicle navigation because it can provide accurate velocity information. As the main system, SINS/DVL integrated navigation is widely used in the underwater vehicle. However, DVL scale factor error, and misalignment with inertial measurement unit (IMU) are the two sources of integration inaccuracy. A novel calibration method aided GNSS is proposed in this paper, which is designed based on velocity observation and position observation respectively. The proposed method combines the Quasi-Newton method and Quaternions method, and we name it Quasi-Newton Quaternion (QNQ) calibration method. In order to verify the performance of the proposed method, another calibration method based on the Kalman filter is introduced in simulation, vehicle tests, and Sea trial. The test results indicate that the proposed calibration method can perform a good estimation of DVL error.

A Quasi-Newton Quaternions Calibration Method for DVL Error Aided GNSS

This paper proposes a new quaternion calibration algorithm to calibrate large misalignment angles between Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL) in SINS/DVL integrated navigation system. A new SINS/DVL/GPS integrated navigation system is derived to complete the SINS alignment and SINS/DVL integrated navigation system calibration. Different from the traditional calibration model, the misalignment angles are described by quaternion, according to the physical properties of misalignment angle and scale factor error, the new measurement equation is derived: zero observation vector is used to estimate the misalignment angles, and velocity error scalar is used to estimate the scale factor error. A switching measurement information Kalman filter is designed to switch between different processes. The performance of the proposed quaternion calibration algorithm is evaluated through simulation comparisons with nonlinear model approaches and experiment. The simulation results demonstrate that the proposed quaternion calibration algorithm has better accuracy and robustness than the nonlinear model approaches. The experiment results show that the proposed quaternion calibration algorithm is effective, the SINS/DVL integrated navigation system positioning error after calibration is less than 1.65% mileage in more than 30km travel.

A Novel Calibration Method of SINS/DVL Integration Navigation System Based on Quaternion

In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the prior scale matrix of the one-step prediction error covariance matrix is adaptively estimated by using the expectation-maximization algorithm. A novel alternate iteration strategy is proposed to reduce the computational complexity of the proposed method. Convergence analysis and theoretical comparisons with the existing advanced adaptive Kalman filtering methods are also provided. Based on this, a new master-slave cooperative localization method is proposed. Lake experiment results of cooperative localization for autonomous underwater vehicles demonstrate the advantages of the proposed method over existing methods. Compared with the cutting-edge adaptive master-slave cooperative localization method, the proposed method has been improved by 22.52% in average localization error but no more than twice computational time is needed.

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.

A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

In navigation of autonomous underwater vehicles (AUVs), the estimation of position is an important issue, especially, when the sensors such as gyroscopes contain a lot of noise, and the velocity information of Doppler velocity log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for gyroscope signal de-noising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this paper, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound, and the existence of the DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions.

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A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

Virtual DVL Reconstruction Method for an Integrated Navigation System Based on DS-LSSVM Algorithm

The main challenge of Strap-down Inertial Navigation System (SINS)/Doppler velocity log (DVL) navigation system is the external measurement noise. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been introduced in the integrated navigation field, the precision and stability of the SHAKF are still the tricky problems to be overcome. The primary aim of this paper is to improve the precision and stability of underwater SINS/DVL system. To attain this, a SINS/DVL tightly integrated model is established, where beam measurements are used without transforming them to 3D velocity. The proposed improved SHAKF algorithm is based on variable sliding window estimation and fading filter. The simulations and vehicle test results demonstrate the effectiveness of the proposed underwater SINS/DVL tightly integrated navigation method based on the improved SHAKF. In addition, the position accuracy of the designed method outperforms that of the SHAKF method.

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Di Wang

Papers

A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

A Quasi-Newton Quaternions Calibration Method for DVL Error Aided GNSS

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Virtual DVL Reconstruction Method for an Integrated Navigation System Based on DS-LSSVM Algorithm

An Improved Adaptive Kalman Filter for Underwater SINS/DVL System