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RODM-Alignment: An Inertial Alignment Algorithm for Sliding Window Reverse optimization Delayed Marginalization

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Peng, Jincheng Zhang, Xiaoli Peng, Xiafu Yao, Pengchao Chen, Jinwen Yang, Gongliu Xiamen Univ Sch Aerosp Engn Dept Automat Xiamen 361102 Fujian Peoples R China Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China

In the initial alignment of the strapdown inertial navigation system, it is difficult to obtain sufficient data information to make the optimized state of convergence in a short time, which cannot realize fast alignment, and the accuracy of the alignment results is poor. The dynamic sliding window is difficult to recover after marginalization, and the linearization points of the Jacobi matrix are inconsistent, leading to the zero-space variation of the information matrix. To address the above problems, we propose an inertial alignment algorithm for sliding window reverse optimization delayed marginalization (RODM) to improve the rapidity and accuracy of the alignment algorithm. The method of storing gyro and accelerometer data during the alignment process and repeatedly solving the stored inertial data using forward and reverse navigation solving and optimization algorithms (OAMs) is used as a way to extend the interval of inertial data collected in a short period of time so that there is enough inertial data to enable the convergence of the state variables of the sliding-window optimization for a short period of time to achieve fast alignment. A delayed marginalization factor is used to update the marginalized prior information, solving the problem that the linearization of the Jacobi matrix must be fixed point to improve the accuracy of the alignment algorithm. We propose an algorithm to improve the accuracy of alignment, which we have verified the rapidity and accuracy of our initial alignment system in the turntable test, where the accuracy of the alignment heading angle reaches 0.136467 mil in 3 min, providing accurate attitude information for navigation and localization.

关键词： Jacobian matrices optimization Accuracy Inertial navigation Gyroscopes Accelerometers Earth factor graph optimization initial alignment reverse navigation state estimation strapdown inertial navigation system

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Visual Heading-Aided Pedestrian Navigation Method Based on factor graph in Indoor Environment

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第1期71卷 1006-1016页

作者： Yuan, Cheng Lai, Jizhou Lyu, Pin Wang, Tao Zhu, Jingyi Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

The main error in traditional inertial pedestrian navigation is caused by unobservable heading errors with only zero velocity constraints. In this article, a human posture model-aided optimized fusion framework based on dominant events for pedestrian navigation is proposed to precisely locate pedestrians. optimization is performed only at the moment of dominant events to reduce computational effort and improve the robustness. This model can fit the preferred heading information between two dominant events for fusion to improve accuracy. The optimization-based framework can also reduce the nonlinear error by relinearizing the state and associating constraints to the historical state to estimate the biases better. Besides, a hybrid visual heading angle estimation method is proposed by leveraging the Manhattan world hypothesis to obtain a drift-free heading angle with a monocular camera. The hypothesis is searched in the designated region based on histogram quality assessment that can improve the robustness and significantly reduce the computational amount. With low-cost devices, the multiple indoor experiments show that the proposed method can achieve 0.11% positioning accuracy comparable to traditional algorithms, which require numerous conditions to be met in good environments, and achieving over 70% improvement in accuracy over traditional methods in complex environments.

关键词： Estimation Cameras Indoor environment Navigation Visualization Histograms optimization factor graph optimization inertial measurement unit inertial pedestrian dead reckoning (IPDR) Manhattan world (MW) monocular camera

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DGVINS: tightly coupled differential GNSS/visual/inertial for robust positioning based on optimization approach

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第8期35卷 086307-086307页

作者： Li, Xiaowan Cheng, Fang Li, Yuanqi Shen, Pengli Hu, Yuhang Lu, Xiaochun Chinese Acad Sci Natl Time Serv Ctr Xian 710600 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Precise Positioning & Timing Technol Xian 710600 Peoples R China

Due to the fragility of single-sensor positioning technology in complex scenarios, especially in complex urban areas, multi-sensor positioning technology is becoming increasingly popular. To further improve the robustness of the positioning system by fully utilizing the information from various sensors, this article proposes a differential-GNSS-visual-inertial navigation system (DGVINS) that tightly fuses differential global navigation satellite system (GNSS), vision and inertial information to provide accurate, robust and seamless position information for intelligent navigation applications. DGVINS effectively utilizes all sensor measurements within the factor graph optimization framework. When using the carrier phase of GNSS, single-epoch ambiguity optimization is employed to prevent cycle slip detection and adapted to complex environments. We conducted experiments on public datasets with various features and compared the performance of simple differential-GNSS (DGNSS), DGNSS + Inertial, and the state-of-the-art GNSS-visual-inertial navigation systems. We also compared the performance of different combinations of GNSS differential factors in various environments. Due to the superiority of differential GNSS and its appropriate integration with visual and inertial measurements, the experimental results demonstrate that DGVINS exhibits significant improvements in accuracy, stability, and continuity in both GNSS-challenged and vision-challenged environments.

关键词： sensor fusion factor graph optimization GNSS visual-inertial odometry single-epoch ambiguity

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An INS/UWB fusion localization scheme for wireless sensor network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第12期35卷 126306-126306页

作者： Wang, Yan Lu, You Gong, Yuxin Northeastern Univ Dept Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Hebei Peoples R China

As the demand for indoor services relying on location information grows, achieving precise indoor positioning is becoming an urgent issue to address. The ultra-wide band (UWB) accuracy can reach centimeter level, but is susceptible to non-line-of-sight (NLOS) effects. The inertial navigation system (INS) is not affected by the environment, but the error will diverge over time. Therefore, this paper focuses on the UWB/INS combined positioning system, which can combine the advantages of both. We propose a loosely-coupled INS/UWB indoor localization scheme based on factor graph optimization (FGO). Firstly, an agglomerative hierarchical clustering based NLOS mitigation method is applied to preprocess the UWB raw measurement data. The position estimates under UWB are then obtained using an improved extended Kalman filter to mitigate NLOS errors and provide more accurate location data for combined navigation. Then, we build the IMU preintegration factor and the zero-bias factor. Finally, FGO is used to fuse the information to get the final location. Designed experiments demonstrate the superiority of the algorithm. The simulation and experimental results show that the proposed scheme outperforms the comparison algorithms.

关键词： wireless sensor network agglomerative hierarchical clustering indoor localization combination positioning factor graph optimization

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factor graph-Based Tightly Coupled RTK/INS/LiDAR System With De-Drifting LiDAR Data Association in Urban Areas

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IEEE SENSORS JOURNAL 2025年第9期25卷 15442-15455页

作者： Wang, Chuji Wang, Pai Wang, Feng Tang, Weiming Geng, Jianghui Wuhan Univ GNSS Res Ctr Wuhan 430079 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Wuhan 430071 Peoples R China

Accurate, reliable, and continuous positioning is crucial for applications like autonomous driving and mobile robots. Integrating multiple sensors, such as the global navigation satellite system (GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR), in a tightly coupled manner has become a promising solution to leverage their complementary advantages. However, LiDAR pose constraints may face significant inconsistencies with GNSS absolute measurements when using the frame-to-map data association method. Alternatively, frame-to-frame LiDAR data association suffers from limited accuracy due to sparse feature point matching. This article presents a tightly coupled GNSS real-time kinematic (RTK)/INS/LiDAR positioning system based on a de-drifting LiDAR data association method. The LiDAR keyframe selection strategy is designed by accounting for the availability and reliability of both GNSS and LiDAR data. A frame-to-last-GNSS-available-frame data association method is developed, utilizing both plane and edge features to construct LiDAR relative constraints. These measurements, alongside INS data and GNSS pseudorange and carrier phase measurements, are integrated within a factor graph optimization framework for pose estimation. Experimental results from an autonomous vehicle in urban environments demonstrate that the proposed tightly coupled system significantly outperforms other integration approaches using various sensor combinations, integration types, and LiDAR data association methods, improving 3-D positioning accuracy in root mean square (rms) from 12.40 m (using GNSS RTK) to 0.20 m. Moreover, the proposed method maintains high-accuracy absolute positioning with a 3-D position error of 0.28 m and a 3-D maximum error of 0.44 m during a 20-s GNSS outage.

关键词： factor graph optimization global navigation satellite system (GNSS) inertial navigation system (INS) light detection and ranging (LiDAR) localization real-time kinematic (RTK) tightly coupled factor graph optimization global navigation satellite system (GNSS) inertial navigation system (INS) light detection and ranging (LiDAR) localization real-time kinematic (RTK) tightly coupled

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UVIO: Adaptive Kalman Filtering UWB-Aided Visual-Inertial SLAM System for Complex Indoor Environments

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REMOTE SENSING 2024年第17期16卷

作者： Li, Junxi Wang, Shouwen Hao, Jiahui Ma, Biao Chu, Henry K. Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China

Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment.

关键词： ultra-wideband (UWB) visual-inertia SLAM RSSI-EOP factor-related gain NLOS identification and correction adaptive Kalman filter factor graph optimization

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Resilient tightly coupled INS/UWB integration method for indoor UAV navigation under challenging scenarios

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Defence Technology（防务技术） 2023年第4期22卷 185-196页

作者： Qian Meng Yang Song Sheng-ying Li Yuan Zhuang Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Southeast UniversityNanjing210096China Department of Mechanical Engineering Hong Kong Polytechnic UniversityHong KongChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina

Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and ***,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging *** deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the *** on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise *** proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging *** to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection *** superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.

关键词： Unmanned aerial vehicle(UAV) Resilient navigation Indoor positioning factor graph optimization Ultra-wide band(UWB)

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A-LIQ: A Warehouse AGV Navigation AIgorithm Based On Laser- InertiaI QR Code Fusion

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第9期27卷 3163-3172页

作者： Wang, Xin Zhang, Bo Zhao, Jianwei You, Hezhen Shenyang Jianzhu Univ Informat & Control Engn Fac Shenyang 110168 Peoples R China Baoding Vocat & Tech Coll Dept Comp Informat Engn Baoding 071051 Hebei Peoples R China Tongji Univ Comp Sci & Technol Shanghai 200092 Peoples R China

The method based on laser inertial navigation technology has been widely used in the navigation field of automatic guided vehicles (AGV) in warehouse workshops. The existing algorithms are prone to scale drift, large cumulative error, and LIDAR degradation leading to serious reduction in the number of sensing points, we aim to design an AGV navigation framework based on the fusion of laser, inertial, and quick response (QR) code technologies (named A-LIQ). First, an inertial measurement unit (IMU) pre-integration model with QR code is proposed, and the obtained QR code constraint information is added between two key frames to form a new composite unit, reducing scale drift, and improving positioning accuracy. Secondly, a local map optimization model is proposed, keyframes and QR codes are selectively introduced, local stratified bundle adjustment (BA) optimization is performed based on sliding windows, and keyframe poses and map point locations are updated. Finally, a LiDAR/IMU/QR code tight coupling optimization method is proposed, and the pre-integration factor, closed-loop factor, QR factor, and laser odometer factor are incorporated into the factor graph system to achieve multi-level data fusion. In this paper, the method is verified on the developed AGV navigation platform, and its performance is evaluated by using measured data and compared with LeGO-LOAM, BALM, LIO-SAM. The results show that the method does not significantly increase the calculation amount. Effectively improve the track closure effect at the closed loop, with lower positioning error, positioning accuracy error is less than 0.02 meters, attitude error is less than 2(degrees).

关键词： Automatic Guided Vehicle (AGV) Navigation IMU Preintegration Laser Odometry QR Code factor graph optimization

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Geometry-free TDRTK: a new time-difference RTK model considering multi-frequency observations

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SURVEY REVIEW 2024年第395期56卷 129-139页

作者： Liu, Chunhe Chai, Hongzhou Xiang, Minzhi Du, Zhenqiang PLA Strateg Support Force Informat Engn Univ Zhengzhou Peoples R China

The time difference RTK (TDRTK) of GNSS can obtain high-precision baseline constraints to control the cumulative error of dead reckoning technology. However, the availability of traditional TDRTK decreases sharply with the increase of time interval. A geometry-free TDRTK method with quality control using multi-frequency observations is proposed to solve this problem. Experimental shows that in the dynamic condition of a stand-alone GNSS receiver, the average and maximum relative position accuracy (10 min and 10 kilometres tests) of the proposed method are improved by 42.2% and 44.2% compared with the existing methods.

关键词： Global Navigation Satellite System (GNSS) factor graph optimization Baseline constraint Protection level Geometry-free time difference RTK

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Virtual-network-reconstruction-based underwater acoustic localization framework: Theory and experiment validation

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OCEAN ENGINEERING 2024年 297卷

作者： Liu, Feng Chen, Huifang Li, Yida Xie, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Zhoushan Ocean Res Ctr Zhoushan 316021 Zhejiang Peoples R China

Due to the harsh underwater environment, it is a challenging issue to realize the high -precision localization for underwater mobile nodes. In this paper, we investigate the problem of asynchronous acoustic localization for underwater mobile nodes, and propose a virtual -network -reconstruction -based underwater acoustic localization framework using a factor graph, named as the VNR-UAL-FG framework. Integrating the beacon network topology and positioning message broadcasting timing, a virtual beacon network is reconstructed incorporating the motion of underwater mobile node and the historical received positioning messages. The VNR-UAL-FG framework consists of three essential modules. The input pre-processing and state estimation module handles data collected from various sensors and conducts state estimation as a motion factor. The estimation module focuses on the acoustic -aided localization to perform a joint localization and time synchronization as an acoustic factor. The optimization module integrates motion factors and acoustic factor to refine the localization results. The effectiveness of proposed framework was validated by simulations and sea experiment. Numerical results show that localization accuracy is improved by more than 60% and 25% compared with modified dead -reckoning method and UKF-based localization framework. And the localization update rate is more than 2 times compared with TDoA-based localization method.

关键词： Underwater localization Underwater acoustic-aided localization Underwater mobile node factor graph optimization Asynchronous

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