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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Wu, Hao Yan, Li Xie, Hong Wei, Pengcheng Dai, Jicheng Gao, Zhao Zhang, Rongling Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Hubei Luojia Lab Wuhan 430079 Peoples R China

ISBN: (纸本)9789819984312;9789819984329

lidar enables fast reconstruction of the real world using high-precision point cloud maps. It usually requires the pose information (also called trajectory) of point clouds obtained by lidar at different times so that all scans are unified in the global coordinate system. Pairwise registration is one of the commonly used technology to estimate the poses of the lidar by measuring the relative pose of the scans and incrementally registering scans into the global map. However, the pose information obtained by pairwise registration largely suffers from low accuracy, inefficiency, and drift in trajectory. In this paper, we present a voxel-based multiview point cloud refinement method, which can effectively maintain the global consistency of point cloud maps. The proposed method store multiple point clouds into a voxel feature map indexed by the hash table, and then construct the factor graph using both point-to-point and point-to-plane factors to optimize the sensor's poses. A multi-scale voxel-grid optimization strategy is also presented to enhance the robustness as well as the accuracy of our algorithm. We conduct extensive experiments on the proposed method to demonstrate its accuracy and efficiency. It shows that the proposed method achieves higher accuracy than the state-of-the-art methods with a reasonable time cost.

关键词： Point cloud registration Multiple overlaps factor graph optimization

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LIDAR-INERTIAL NAVIGATION BASED ON MAP AIDED DISTANCE CONSTRAINT AND factor graph optimization 5

LIDAR-INERTIAL NAVIGATION BASED ON MAP AIDED DISTANCE CONSTR...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Ai, Mengchi Elhabiby, Mohamed Hokmabadi, Ilyar Asl Sabbaghian El-Sheimy, Naser Univ Calgary Dept Geomat Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Micro Engn Tech Inc Calgary AB Canada

The simultaneous localization and mapping (SLAM) is one of the well-developed positioning technology that provides high accuracy and reliability positioning for automatic vehicles and robotics applications. Integrating Light Detection and Ranging (LiDAR) with an Inertial Measurement Unit (IMU) has emerged as a promising technique for achieving stable navigation results in dense urban environments, outperforming vision-based or pure Inertial Navigation System (INS) solutions. However, conventional LiDAR-Inertial SLAM systems often suffer from limited perception of surrounding geometric information, resulting in unexpected and accumulating errors. In this paper, we proposed a LiDAR-Inertial SLAM scheme that utilizes a prior structural information map which is generated from opensource OpenStreetMap (OSM). In contrast to conventional solutions of OSM-aided SLAM approaches, our method extracts the vectorized models of road and building and synthetically generates dense point maps for LiDAR registration. Specifically, a structural map processing module extracts the road models and building models from OSM and generates a structure information map (SIM) with dense point clouds. Secondly, a map aided distance (MD) constraint is calculated by registering selected keyframes and the prior SIM. Finally, a factor graph optimization (FGO) algorithm is involved to integrate the relative transformation obtained from LiDAR odometry, IMU pre-integration, and the map aided distance constraints. To evaluate the proposed LiDAR-based positioning accuracy, experimental evaluation is implemented in an opensource dataset collected in the urban canyon environments. Experimental results demonstrates that with the help of the proposed MD constraint, the LiDAR-based navigation solution can achieve accurate positioning, with a root mean square deviation (RSME) of 4.7 m.

关键词： Automatic driving LiDAR-IMU integration Sensor fusion factor graph optimization OpenStreetMap map aided navigation

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IMPROVING GNSS POSITIONING RELIABILITY AND ACCURACY BASED ON factor graph optimization IN URBAN ENVIRONMENT 5

IMPROVING GNSS POSITIONING RELIABILITY AND ACCURACY BASED ON...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Zhang, Y. Zhu, F. Zhang, X. Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Wuhan Univ Chinese Antarctic Ctr Surveying & Mapping Wuhan 430079 Peoples R China

Global navigation satellite system (GNSS) can provide global, precise, and continuous positioning in open-sky environments. However, urban environments with frequent outliers and cycle slips degrade the traditional Extended Kalman Filter (EKF) positioning performance. The susceptibility of EKF to outliers is attributed to its inherent structure. To mitigate, the GNSS positioning based on the factor graph optimization (FGO) structure is adopted. FGO can enhance time correlation among observations and enable the updating of historical information, thereby improving resistance against outliers. In this study, we proposed a single-differenced GNSS-FGO model instead of the double-differenced model to preserve the sparsity of FGO, and outlier detection and PAR methods are employed to ensure urban positioning performance. To evaluate the proposed structure, experiments are conducted in both urban and open-sky environments. The results demonstrate the improvement of positioning accuracy and reliability, compared to EKF.

关键词： factor graph optimization Single-differenced Model Urban Environments Outlier Detection GNSS Positioning

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Impact of the Earth Rotation Compensation on MEMS-IMU Preintegration of factor graph optimization

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IEEE SENSORS JOURNAL 2022年第17期22卷 17194-17204页

作者： Tang, Hailiang Zhang, Tisheng Niu, Xiaoji Fan, Jing Liu, Jingnan Wuhan Univ GNSS Res Ctr Wuhan 430079 Peoples R China

In filtering-based global navigation satellite system (GNSS) / inertial navigation system (INS) integrated navigation systems, the precise INS mechanization has fully considered the impact of the Earth rotation, Coriolis acceleration, etc. However, most inertial measurement unit (IMU) preintegration models in factor graph optimization (FGO)-based integrated navigation systems are rough and ignore these factors. We propose an FGO-based GNSS/INS integrated navigation system to analyze and evaluate the impact of the Earth rotation on MEMS-IMU preintegration. The proposed FGO-based integration is based on a refined IMU preintegration, in which the Earth rotation is compensated. The proposed GNSS/INS integration is a sliding-window optimizer in which the GNSS positioning and the IMU preintegration are tightly fused within the FGO framework. The simulated GNSS outage in field tests, an effective method, is adopted to evaluate the IMU preintegration quantitatively. The results demonstrate that the refined IMU preintegration can achieve the same accuracy as the precise INS mechanization. In contrast, the rough IMU preintegration and INS mechanization without compensating for the Earth rotation yields notable accuracy degradation. When the GNSS-outage time is 60 seconds, the degradation can be 200% for the industrial-grade MEMS module and more than 10% for the consumer-grade MEMS chip. Besides, the degradation can be more significant if the GNSS-outage time is longer. The proposed FGO-based GNSS/INS integration (https://***/i2Nav-WHU/OB_GINS) and the employed datasets (https://***/i2Nav-WHU/awesome-gins-datasets) are open-sourced on GitHub.

关键词： Earth Micromechanical devices Global navigation satellite system Navigation Sensors optimization Gravity MEMS-IMU preintegration factor graph optimization GNSS INS integration

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Continuous-Time factor graph optimization for Trajectory Smoothness of GNSS/INS Navigation in Temporarily GNSS-Denied Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9115-9122页

作者： Zhang, Haoming Xia, Xiao Nitsch, Maximilian Abel, Dirk Rhein Westfal TH Aachen Fac Mech Engn Inst Automat Control D-52074 Aachen Germany

For autonomous systems, the smoothness of estimated trajectories is essential to ensure robust and performant vehicle control. Unfortunately, approaches for state estimation using Kalman filters are sensitive to measurement outliers that can diverge dramatically. We propose a state-estimation algorithm using factor graph optimization (FGO) in continuous-time for GNSS/INS navigation systems to track this problem. The focus is on the smoothness of the estimated trajectory in environments where the GNSS observations become temporarily unreliable. Therefore, an inland shipping scenario with bridge crossings is selected as an application example. To estimate the trajectory in continuous-time, we integrate a White-Noise-On-Acceleration (WNOA) motion prior factor based on Gaussian process regression between successive states. Our results show a 30% improvement in accuracy and increased smoothness of the estimated trajectory with FGO compared to Kalman filtering.

关键词： Localization Sensor Fusion Marine Robotics factor graph optimization GNSS INS-Integration

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A factor graph optimization mapping based on normal distributions transform

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第3期30卷 1127-1141页

作者： Zhong, Kedi Liu, Yuansheng Yang, Jiansuo Lu, Ming Zhang, Jun Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing Peoples R China Beijing Union Univ Coll Robot Beijing Peoples R China Beijing Union Univ Coll Appl Sci & Technol Beijing Peoples R China

This paper aims to achieve highly accurate mapping results and real time pose estimation of autonomous vehicle by using the normal distribution transform (NDT) algoritm. A factor graph optimization algorithm (FGO-NDT) is proposed to address the poor real-time performance and pose drift errors of the NDT algorithm. Smooth point cloud data are obtained by multisensor calibration and data preprocessing. NDT registration is then used for lidar odometry and feature matching. The global navigation satellite system (GNSS) data and loop detection results are added to the factor graph framework as the pose constraint factors to optimize the pose trajectory and eliminate the pose drift error generated during mapping. In addition, a sliding window method is used for map registration to extract a local map to shorten the map loading time. Thus, the real-time performance of creating point cloud maps of large scenes is significantly improved. Several experiments are conducted in different environments to verify the accuracy and performance of the FGO-NDT. The experimental results demonstrate that the proposed method can eliminate the pose estimation error caused by drift, improve the local structure, and reduce and root mean square error.

关键词： NDT factor graph optimization sliding window point cloud map

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GNSS Outlier Mitigation via Graduated Non-Convexity factor graph optimization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第1期71卷 297-310页

作者： Wen, Weisong Zhang, Guohao Hsu, Li-Ta Hong Kong Polytech Univ Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.

关键词： Global navigation satellite system Receivers Satellites Weight measurement Estimation Three-dimensional displays Position measurement GNSS NLOS navigation factor graph optimization graduated non-convexity adaptive tunning non-convex Geman McClure urban canyons

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Integrity-constrained factor graph optimization for GNSS Positioning

Integrity-constrained Factor Graph Optimization for GNSS Pos...

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IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Xia, Xiao Hsu, Li-Ta Wen, Weisong Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665417723

The concept of global navigation satellite system (GNSS) integrity refers to the measure of trust of the GNSS positioning solution, which is vital for safety-critical applications such as aviation and autonomous driving. While integrity monitoring was firstly introduced and widely applied in the GNSS aviation field, it is not suitable for GNSS positioning in urban scenarios due to unique circumstances such as limited satellite visibility, strong multipath and non-line-of-sight (NLOS) effects. For example, the direct exclusion of the GNSS multipath and NLOS would significantly degrade the geometry constraints, thus leading to highly conservative integrity monitoring (IM). As a result, the limited GNSS measurement redundancy and the inaccurate measurement uncertainty modeling in urban canyons will severely degrade the performance of both the GNSS positioning and integrity monitoring. To alleviate these issues, this paper proposed an integrity-constrained factor graph optimization (FGO) for GNSS positioning with the help of switchable constraints. Compared to the conventional GNSS IM methods which consider measurements in single epoch or two successive epochs, the proposed method improves the measurement redundancy by the factor graph structure. Meanwhile, the switch variable, which is introduced by switchable constraints and connected with each pseudorange measurement, can not only estimate the measurement uncertainties, but also satisfying the Chi-square testing of the conventional fault detection and exclusion (FDE) while maintaining satellite geometry. In particular, the calculated protection levels consider the effect of switch variables, hence bound the position error more accurately. The performance of this proposed method is evaluated on open-sky dataset with manually injected biases with gaussian random noise.

关键词： integrity monitoring factor graph optimization switch variables fault detection and exclusion urban positioning

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A Multi-vehicle Cooperative Positioning Method Based on factor graph optimization Using the Error Information of Cooperators 36

A Multi-vehicle Cooperative Positioning Method Based on Fact...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Wang, Tongtong Zhao, HongBo Zhuang, Chen Hu, Shan Beihang University China

ISBN: (纸本)9780936406350

The positioning technology based on the Global Navigation Satellite System (GNSS) often encounters significant challenges in complex environments, such as buildings, urban canyons, and tunnels, due to multipath effects and non-line-of-sight (NLOS) signals, making it difficult to meet the requirements of high-precision vehicle positioning. Cooperative positioning (CP) methods can enhance the accuracy and reliability of vehicular positioning in GNSS-degraded environments. However, traditional CP methods often overlook the positioning error information of cooperators, leading to a detrimental impact on the overall accuracy of CP systems. In this paper, we propose a novel CP method that integrates real-time error information of cooperators to address this limitation. To improve the reliability and accuracy of the CP system, we utilizes factor graph optimization (FGO) to fuse the available information. Unlike traditional algorithms that rely solely on current epoch observations, FGO algorithms employ global optimization algorithms to overcome the limitations. The key innovation of our proposed CP method lies in the real-time evaluation of vehicle accuracy and the utilization of this information to constrain the uncertainty of the cooperators in challenging urban environments. Numerical results and comparisons validate the feasibility and superiority of the proposed method. © 2023 Proceedings of the 36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023. All rights reserved.

关键词： Cooperative Positioning factor graph optimization GNSS

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Positioning Based on factor graph optimization with GNSS Pseudorange Measurements from Smartphones 6

Positioning Based on Factor Graph Optimization with GNSS Pse...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Li, Song Lin, Honglei Tang, Xiaomei Ma, Pengcheng Wang, Feixue National University of Defense Technology College of Electronic Science and Technology Changsha China

ISBN: (纸本)9798350313444

The Global Navigation Satellite System (GNSS) is playing an increasingly critical role in providing positioning, navigation, and timing services. This work focuses on the positioning performance of GNSS receivers. The least squares (LS) and Kalman filter (KF) approaches are widely used in the GNSS community. However, these methods have their shortcomings because not all information is used. Specifically, in the LS, only the measurement information is used to solve for the position. Compared to the LS, the KF uses the dynamic model to exploit the relationship between the position in adjacent epochs. However, only the information in two adjacent epochs is used, while historical information is discarded. The factor graph optimization (FGO) is presented to overcome this problem. The state transition and measurement models are considered constraints and used to construct the cost function. The optimization problem is solved to obtain the optimal position estimate. The field test with a smartphone is carried out to confirm the superiority of the FGO over the LS and KF methods. © 2023 IEEE.

关键词： factor graph optimization GNSS Kalman filter least squares positioning smartphone

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