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An Algorithm of factor graph optimization for 3D Map of Unmanned Exploration Vehicle in Tunnel Environment

JOURNAL OF ROBOTICS 2025年第1期2025卷

作者： Xie, Jian Li, Rao Li, Jing Xu, Aoshu Chongqing Polytech Univ Elect Technol Sch Elect & Internet Things Chongqing 401331 Peoples R China

The simultaneous localization and mapping (SLAM) technology based on LiDAR has not only achieved real-time localization of unmanned exploration vehicles in unknown environments but also efficiently obtained three-dimensional geospatial information of the environment, attracting widespread attention in the field of unmanned driving. In tunnels with degraded geometric structures, poor lighting conditions, and a lack of GPS signal, relying solely on geometric information cannot register point clouds. Therefore, traditional LiDAR SLAM algorithms are difficult to apply in tunnels. To address this issue, this paper proposes a factor graph-optimized SLAM algorithm based on the fusion of iterative closest point (ICP) and normal distributions transform (NDT) algorithms. The front-end point cloud registration module and the back-end construction algorithm based on filtering and graph optimization are designed. To verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on the KITTI dataset and a real tunnel. The results show that the average processing time of the proposed method in this article is about 75 ms, which can meet the real-time requirements of unmanned exploration vehicles.

关键词： 3D map factor graph optimization LiDAR SLAM

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Tightly coupled integration of monocular visual-inertial odometry and UC-PPP based on factor graph optimization in difficult urban environments

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GPS SOLUTIONS 2024年第1期28卷 45页

作者： Pan, Cheng Li, Fangchao Pan, Yuanxin Wang, Yonghui Soja, Benedikt Li, Zengke Gao, Jingxiang China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou Peoples R China Swiss Fed Inst Technol Inst Geodesy & Photogrammetry CH-8093 Zurich Switzerland Northwest A&F Univ Coll Forestry Yangling Peoples R China

The emergence of low-cost micro-electro-mechanical system inertial measurement units, cameras and global navigation satellite system (GNSS) receivers has promoted the research of multisensor fusion positioning. With the rapid development of chip technology, it has become a trend for low-cost GNSS chips to provide multi-frequency carrier phase observations while supporting multiple constellations. To enhance the positioning performance of multi-frequency and multi-system precise point positioning (PPP) in the difficult urban environment, we propose a tightly coupled system of monocular visual-inertial odometry (MVIO) and uncombined PPP (UC-PPP) based on factor graph optimization. The initialization of MVIO and UC-PPP adopts a coarse-to-fine approach to correct the transformation of local and global frames online. Moreover, the sliding window and marginalization methods are adopted to retain the constraints between adjacent observations and eliminate useless observations in the window. The pedestrian and vehicle tests in urban environments verify the performance of the proposed method. Compared with open-source software GVINS, the positioning accuracy of the proposed method has been further improved by using carrier phase observations with higher measurement accuracy. Compared with PPP alone, the improvement of the proposed method for the low-speed and short-distance pedestrian test in the east, north, and up directions is 73.3, 54.8 and 62.7%, respectively, while the improvement for the high-speed and long-distance vehicle test is 63.0, 59.3 and 70.5%, respectively. Experiment results show that the proposed method has better positioning accuracy and continuity in difficult urban environments.

关键词： Monocular visual-inertial odometry factor graph optimization Uncombined PPP Tightly coupled system

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A factor graph optimization-Based In-Motion Alignment Method for INS/DVL Integration

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18452-18459页

作者： Zhang, Liang Zhang, Tao Wei, Hongyu Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Technol Minist Educ Nanjing 210096 Peoples R China

An accurate initial attitude is of great importance for the integration of Doppler velocity log (DVL) and the strapdown inertial navigation system (SINS). The speed and accuracy of the initial alignment are two important indicators. The process is usually divided into two stages, coarse alignment and fine alignment, to ensure the speed and precision of the alignment. To simplify the alignment steps and improve accuracy, a factor graph optimization(FGO)-based in-motion alignment method is proposed in the paper. First, an IMU factor based on the exact preintegration measurement model is proposed, which is especially suitable for initial attitude alignment of high precision strapdown inertial navigation system. Second, a nonlinear optimization problem based on graph model is constructed with the IMU factor and DVL factor. An incremental fixed lag smoothing method is adopted to solve the nonlinear optimization problem. Due to multiple iterations and batch optimization of the historical data, it can be superior to the traditional alignment method. Both the simulation and field tests show that FGO-based alignment speed is fast enough. It is comparable to that of the coarse alignment and faster than the filter-based method. Besides, the alignment accuracy is still the highest. It confirmed that FGO-based alignment method has great application potential in INS/DVL integration.

关键词： Accuracy Navigation optimization Instruments Convergence Vectors Kalman filters Initial alignment strapdown inertial navigation system doppler velocity log factor graph optimization IMU preintegration

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A High-Precision LiDAR-Inertial Odometry via Kalman Filter and factor graph optimization

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IEEE SENSORS JOURNAL 2023年第11期23卷 11218-11231页

作者： Tang, Jiaqiao Zhang, Xudong Zou, Yuan Li, Yuanyuan Du, Guodong Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing 100081 Peoples R China

For simultaneous localization and mapping (SLAM) in large-scale scenarios, the influence of long-distance and high-speed motion cannot be ignored because the risk of huge odometry drift will increase. To solve this problem, we propose the LiDAR -inertial odometry (LIO) method via Kalman filter and factor graph optimization (LIO-FILO), which provides real-time, high-frequency, and high-precision odometry. The LIO system consisting of three modules is established to fit various application scenarios. The state estimation module provides the pose states to be optimized and receives timely feedback from the pose optimization module. In the loop closure module, LIO-FILO constructs a multilayer structure loop closure detection method, including different schemes of detection, and the loop closure factor is constructed by the pose transformation matrix calculated by ICP. In the pose optimization module, the fast-build adjacent constraint factors and the loop closure factors are added to the factor graph to get the optimized result based on the GTSAM library. The real-world experiments show that LIO-FILO can mitigate the odometry drift to achieve accurate mapping results and obtain higher precision odometry compared with the existing advanced SLAM methods.

关键词： optimization Laser radar State estimation Point cloud compression Simultaneous localization and mapping Kalman filters Sensors factor graph optimization LiDAR-inertial odometry (LIO) loop closure simultaneous localization and mapping (SLAM)

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A post-processing algorithm based on factor graph optimization for autonomous underwater vehicle integrated navigation

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第4期45卷 595-607页

作者： Ma, Xiao-Shuang Liu, Xi-Xiang Li, Chen-Long Sheng, Guang-Run Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Minist Educ Key Lab Microinertial Instrument & Adv Nav Techno Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

Aiming at the requirements of offline high-precision navigation and positioning in the mapping of ocean floor, survey, and other fields for autonomous underwater vehicle (AUV)integrated navigation system, a post-processing algorithm based on factor graph optimization is proposed in this paper. First, the factor graph model is established by the factor graph formulation to address the asynchronous and heterogeneous problem of multi-source information fusion. Then the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU factors between two adjacent measurements to further aid high-rate navigation solutions. Second the forward and backward message passing processes are performed independently using the sum-product algorithm in the factor graph model. Finally, the optimal navigation solution is obtained by weighted smoothing the results of the forward and backward message passing processes. Simulation results show this algorithm proposed in this paper provides more accurate navigation accuracy and smoothness, especially in the case of signals loss or sensors fault. Compared with the federal Kalman filtering method, the horizontal positioning errors of the un-simplified factor graph, the simplified factor graph, and the factor graph optimization are improved by about 30%, 34%, and 80%, respectively. The semi-physical environment results also clearly verify the reliability and effectiveness of the proposed method, and its horizontal positioning accuracies are kept within -2 similar to 2 m.

关键词： autonomous underwater vehicle multi-source information fusion sum-product algorithm factor graph optimization integrated navigation system

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factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

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NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION 2021年第2期68卷 315-331页

作者： Wen, Weisong Pfeifer, Tim Bai, Xiwei Hsu, Li-Ta Hong Kong Polytech Univ Hung Hom Hong Kong Peoples R China Tech Univ Chemnitz Chemnitz Germany

factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real-time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO-based estimator into an "EKF-like estimator." In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation;and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

关键词： extended Kalman filter factor graph optimization GNSS INS integration navigation positioning urban canyons window size

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A Voxel-Based Multiview Point Cloud Refinement Method via factor graph optimization 6th

A Voxel-Based Multiview Point Cloud Refinement Method via Fa...

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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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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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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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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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