Fujun Pei

Beijing University of Technology

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Shunan Yin

Beijing University of Technology

Mingjun Zhu

China University of Petroleum, East China

Mei Wu

Wuhan University

Siyuan Li

Harbin Institute of Technology

Desen Zhu

East China Jiaotong University

Mingjie Shi

Pacific Northwest National Laboratory

Su Yang

Beijing University of Technology

Ning Jiang

Sichuan University

Haonan Song

Nankai University

Zhu Li

Ocean University of China

Cooperative Institutions

Beijing University of Technology

Beijing Academy of Artificial Intelligence

Chinese Academy of Sciences

Beijing Institute of Technology

Harbin Institute of Technology

Capital Medical University

Peking University

University of Chinese Academy of Sciences

Sun Yat-sen University

China University of Mining and Technology

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Initial Self-Alignment for Marine Rotary SINS Using Novel Adaptive Kalman Filter

Mathematical Problems in Engineering (2015)

Fujun Pei Zhu Li Jian Hui Zhao

The accurate initial attitude is essential to affect the navigation result of Rotary Strapdown Inertial Navigation System (SINS), which is usually calculated by initial alignment. But marine mooring Rotary SINS has to withstand dynamic disturbance, such as the interference angular velocities and accelerations caused by surge and sway. In order to overcome the limit of dynamic disturbance under the marine mooring condition, an alignment method using novel adaptive Kalman filter for marine mooring Rotary SINS is developed in this paper. This alignment method using the gravity in the inertial frame as a reference is discussed to deal with the lineal and angular disturbances. Secondly, the system error model for fine alignment in the inertial frame as a reference is established. Thirdly, PWCS and SVD are used to analyze the observability of the system error model for fine alignment. Finally, a novel adaptive Kalman filter with measurement residual to estimate measurement noise variance is designed. The simulation results demonstrate that the proposed method can achieve better accuracy and stability for marine Rotary SINS.

Mooring

Observability

10.1155/2015/320536

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Citations (14)

Self-alignment for strapdown INS using real-time adaptive filter

Jian Hui Zhao Fujun Pei Hongyun Liu

As it is difficult to get an accurate mathematical noise model under various dynamic interference conditions, Strap-down Inertial Navigation System (SINS) was difficult to realize self-alignment. Although the fuzzy adaptive Kalman filter can be used to realize the SINS self-alignment, the algorithm complexity is high and the error term is unstable. To solve this problem, a novel self-alignment method using real-time adaptive filter was developed in this paper. This method is based on the theory of fuzzy adaptive adjustment and used the filter stability as the criterion. The exponential function was used to replace the fuzzy inference calculation, which eliminated the complex process of fuzzy, fuzzy inference and de-fuzzy. The stability and accuracy of the adaptive filter was also improved. Finally, the simulation process was used to test the algorithm of this method, and the results demonstrated that the self-alignment using real-time adaptive filter method reduced the complexity of the algorithm and had better stability and alignment accuracy.

10.1109/cgncc.2016.7829101

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Citations (3)

Improved visual-inertial navigation system using self-adjusting entropy difference

Fujun Pei Di Wang Haonan Song

The visual-inertial navigation system of LK-Optical Flow is very sensitive to the environment of different light intensity. In this paper, an adaptive VINS, based on image entropy difference is proposed, which can adjust the emphasis of the weight in the optimization function at any time. The algorithm uses the entropy theory of continuous image to change little, constructs the weight factor of the visual estimation in the optimization function, adjusts the weight of IMU and visual online, and realizes the adaptive estimation of the position and attitude of the object. The method is applied to VINS, to overcome the problem that the poor image quality of the system leads to the degradation of system performance. Finally, the outdoor experimental results verify the performance of the proposed adaptive entropy difference VINS(EE-VINS).

10.1109/cac48633.2019.8996347

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In-Motion Initial Alignment Using State-Dependent Extended Kalman Filter for Strapdown Inertial Navigation System

IEEE Transactions on Instrumentation and Measurement (2020)

Fujun Pei Su Yang Shunan Yin

The in-motion alignment of the strapdown inertial navigation system (SINS) is still a challenging problem due to the complex environmental changes that occur during carrier movement. This article proposes a novel initial alignment method by integrating the advantages of the Special Orthogonal Group of order 3 [SO(3)] representation and the optimal attitude estimation alignment idea. In this method, the SO(3) is used to represent the initial attitude matrix, and the optimal attitude estimation alignment model is established by analyzing the influence of the sensor error during the calculation process. Next, state-dependent extended Kalman filter is investigated by mapping the SO(3) equations to the Lie algebra space, and the Lagrange formula is used to separate the state-dependent errors in the Lie algebraic filtering equation. The simulation and experimental results indicate that the proposed initial alignment method can achieve better alignment accuracy and shorter alignment time than existing methods and can fulfill the in-motion alignment tasks for SINSs in various environments.

State-space representation

Representation

10.1109/tim.2020.3027405

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Citations (26)

A Novel Coarse Alignment Method for SINS Using Special Orthogonal Group Optimal Estimation

Sensors (2020)

Fujun Pei Su Yang Desen Zhu Shunan Yin

Aimed at the alignment problem of strapdown inertial navigation system (SINS) on the swing base, a novel coarse alignment method using special orthogonal group optimal estimation is proposed. There are two main contributions in this paper. First, based on the Lie group differential equation, the rotation matrix is updated directly by using error Lie algebra, which avoids the non-convexity of traditional methods and the need for non-collinear vector observation. Second is that a novel optimal estimation method is developed by using the exact error Lie algebra, which is calculated based on the physical definition of Lie algebra, as the innovation term to compensate the initial special orthogonal group in the estimation process. The asymptotic convergence of the proposed optimal estimation method is proved by Lyapunov's second law. The simulation and experimental results demonstrate that the proposed method exhibits better performance than existing methods in alignment accuracy and time, which can achieve the self-alignment of SINS on the swing base.

Rotation matrix

Orthogonal group

Base (topology)

Optimal estimation

Orthogonal matrix

10.3390/s20205740

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Dynamic SLAM System Using Histogram-based Outlier Score to Improve Anomaly Detection

2021 China Automation Congress (CAC) (2021)

Fujun Pei Miao Zhu Jinghui Wang

It is well known that the traditional visual SLAM systems have performed well in a relatively ideal environment, but for the dynamic environment, there are some problems in the instance segmentation algorithm such as incomplete segmentation or wrong segmentation which will result in outliers. Aiming at this problem, this paper proposed a dynamic SLAM system using Histogram-based Outlier Score (HBOS) to improve the reprojection residual constraint to increase the robustness in removing outliers. In this method, HBOS method is used to process the reprojection residual, and the feature association data obtained after the segmentation mask processing is further filtered to improve the robustness. This method can combine the semantic segmentation network with traditional geometric methods to solve the data association problem caused by the wrong information generated from the dynamic objects. To verify the effectiveness of the proposed method, the experiment is carried out on the public TUM data set and the results show that the proposed method improves the accuracy of SLAM positioning in the dynamic environment.

Robustness

10.1109/cac53003.2021.9728124

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An Improved FastSLAM System Based on Distributed Structure for Autonomous Robot Navigation

Journal of Sensors (2014)

Fujun Pei Haoyang Li Yu-hang Cheng

Fast simultaneous localization and mapping (FastSLAM) is an efficient algorithm for autonomous navigation of mobile vehicle. However, FastSLAM must reconfigure the entire vehicle state equation when the feature points change, which causes an exponential growth in quantities of computation and difficulties in isolating potential faults. In order to overcome these limitations, an improved FastSLAM, based on the distributed structure, is developed in this paper. There are two state estimation parts designed in this improved FastSLAM. Firstly, a distributed unscented particle filter is used to avoid reconfiguring the entire system equation in the vehicle state estimation part. Secondly, in the landmarks estimation part, the observation model is designed as a linear one to update the landmarks states by using the linear observation errors. Then, the convergence of the proposed and improved FastSLAM algorithm is given in the sense of mean square. Finally, the simulation results show that the proposed distributed algorithm could reduce the computational complexity with high accuracy and high fault-tolerance performance.

Feature (linguistics)

Navigation System

10.1155/2014/456289

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Citations (12)

Advanced distributed unscented particle filter for simultaneous localization and mapping

WIT transactions on engineering sciences (2013)

Fujun Pei Mei Wu Simin Zhang

Welcome to the WIT Press eLibrary - the home of the Transactions of the Wessex Institute collection, providing on-line access to papers presented at the Institute's prestigious international conferences and from its State-of-the-Art in Science & Engineering publications.

Line (geometry)

Particle (ecology)

10.2495/icfcit131101

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Rapid Initial Self-Alignment Method Using CMKF for SINS Under Marine Mooring Conditions

IEEE Sensors Journal (2021)

Fujun Pei Shunan Yin Su Yang

To address the initial self-alignment problem of strapdown inertial navigation system under marine mooring conditions, a rapid initial self-alignment method based on the constraint matrix Kalman filter (CMKF) is proposed in this paper. The novelties of this method are two-fold. First, based on the Lie group differential equation, a one-step direct self-alignment model without coarse alignment process is designed directly based on a special orthogonal group of rigid-body rotations. In addition, to improve the alignment accuracy, the sensor biases are augmented into the state matrix to be estimated and compensated during the alignment process. Second, because the state of the proposed model is a matrix containing a special orthogonal group, a CMKF is developed to ensure the estimated accuracy. And a Lagrange function is designed in the CMKF to maintain the orthogonality of the special orthogonal group during the filtering process. The simulation and experimental results demonstrate that the proposed method exhibits better performance than existing methods in alignment accuracy and time, which can achieve the self-alignment of SINS under marine mooring conditions.

Orthogonality

Mooring

Matrix (chemical analysis)

10.1109/jsen.2021.3055536

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Citations (7)

Federated Fingerprint Localization Method Based on Adaptive Aggregation and Feature Alignment

IEEE Wireless Communications Letters (2024)

Fujun Pei Mingjie Shi Yunpeng Xie

Feature (linguistics)

10.1109/lwc.2024.3509685

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