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Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on graph optimization

REMOTE SENSING 2025年第5期17卷 812-812页

作者： Zhang, Baoxiang Yang, Cheng Xiao, Guorui Li, Peigong Xiao, Zhengyang Wei, Haopeng Liu, Jialin China Univ Geosci Sch Land Sci & Technol Beijing 100083 Peoples R China Informat Engn Univ Sch Surveying & Mapping Zhengzhou 450001 Peoples R China Chinese Acad Surveying & Mapping Beijing 100039 Peoples R China

Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation.

关键词： PPP graph optimization LiDAR inertial odometry multi-sensor fusion

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A sliding window graph optimization algorithm based on marginalization theory for cooperative navigation

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 046315-046315页

作者： Cai, Qingzhong Yang, Qian Tu, Yongqiang Niu, Haofei Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Tianmushan Lab Hangzhou 311115 Peoples R China Jimei Univ Coll Marine Equipment & Mech Engn Xiamen 361021 Peoples R China Chinese Aeronaut Estab Beijing 100012 Peoples R China

Cooperative navigation based on a graph optimization algorithm (GOA) is a promising method for multiagent systems to provide accurate location information. However, this method exhibits limited real-time capability. To address this issue, in this paper, we propose a sliding window GOA (SW-GOA) based on marginalization theory for cooperative navigation. The proposed SW-GOA incorporates relative distance information from ultra-wideband ranging sensors and dead reckoning information from inertial measurement units as factor nodes in the factor graph model. We apply marginalization theory to introduce a sliding window (SW) into the graph model, mitigating the problem of increasing complexity over time encountered by conventional GOA methods. By applying a Schur complement to the information matrix, historical states are marginalized while retaining effective constraints to enhance accuracy. Additionally, we utilize incremental optimization based on the QR (QR decomposition) update method to further accelerate the algorithm. Simulations and experiments are conducted to validate the proposed SW-GOA for cooperative navigation. The experimental results show that the positioning accuracy of the proposed method is improved by more than 20% compared with the extended Kalman filter method, and the computational efficiency is increased by 82.8% compared with traditional GOA methods.

关键词： cooperative navigation factor graph graph optimization sliding window marginalization theory

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DL-SLOT: Tightly-Coupled Dynamic LiDAR SLAM and 3D Object Tracking Based on Collaborative graph optimization

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1017-1027页

作者： Tian, Xuebo Zhu, Zhongyang Zhao, Junqiao Tian, Gengxuan Ye, Chen Tongji Univ Sch Elec & Informat Engn Dept Comp Sci & Technol Shanghai 200070 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200070 Peoples R China Tongji Univ Inst Intelligent Vehicles Shanghai 200070 Peoples R China

Ego-pose estimation and 3D object tracking are two critical problems that autonomous driving systems must solve. The solutions to these problems are usually based on their respective assumptions, i.e., the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption in object tracking. However, these assumptions may not hold in dynamic road scenarios, where SLAM and object tracking are closely correlated. Therefore, we propose DL-SLOT, a tightly-coupled dynamic LiDAR SLAM and 3D object tracking method that addresses both problems simultaneously. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment in a unified optimization framework. First, we use object detection to identify all points belonging to potentially dynamic objects. Subsequently, LiDAR odometry is performed using the filtered point cloud. Simultaneously, we propose a sliding window-based 3D multi-object tracking method considering the historical trajectories of the tracked objects. The states of the ego-vehicle, stationary objects, and dynamic objects are jointly estimated by the sliding window-based collaborative graph optimization to achieve SLOT. True stationary objects are restored from the potentially dynamic object set to join the optimization. Finally, a global pose graph is implemented to eliminate the accumulated error. Experiments on the KITTI datasets demonstrate that our method achieves better accuracy than SLAM and 3D object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually beneficial, and dramatically improves the robustness and accuracy of SLAM and object tracking in dynamic road scenarios.

关键词： Simultaneous localization and mapping Vehicle dynamics Laser radar Three-dimensional displays Object tracking optimization Odometry LiDAR SLAM multi-object tracking graph optimization

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BayGO: Decentralized Bayesian Learning and Information-Aware graph optimization Framework

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2101-2116页

作者： AlShammari, Tamara Weeraddana, Chathuranga Bennis, Mehdi Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

Multi-agent Decentralized Learning (MADL) is a scalable approach that enables agents to learn based on their local datasets. However, it presents significant challenges related to the impact of dataset heterogeneity and the communication graph structure on learning speed, as well as the lack of a robust method for quantifying prediction uncertainty. To address these challenges, we propose BayGO, a novel fully-decentralized multi-agent local Bayesian learning with local averaging, usually referred to as non-Bayesian social learning, together with graph optimization framework. Within BayGO, agents locally learn a posterior distribution over the model parameters, updating it locally using their datasets and sharing this information with their neighbors. We derive an aggregation rule for combining received posterior distributions to achieve optimality and consensus. Moreover, we theoretically derive the convergence rate of agents' posterior distributions. This convergence rate accounts for both network structure and information heterogeneity among agents. To expedite learning, agents employ the derived convergence rate as an objective, optimizing it with respect to the network structure alternately with their posterior distributions. As a consequence, agents can successfully fine-tune their network connections according to the information content of their neighbors. This leads to a sparse graph configuration, where each agent communicates exclusively with the neighbor that offers the highest information gain, enhancing communication efficiency. Our simulations corroborate that the BayGO framework accelerates learning compared to fully-connected and star topologies owing to its capacity for selecting neighbors based on information gain.

关键词： Bayesian learning distributed learning social learning graph optimization information heterogeneity KL divergence multi-agent systems

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RC-SLAM: Road Constrained Stereo Visual SLAM System Based on graph optimization

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SENSORS 2024年第2期24卷 536页

作者： Zhu, Yuan An, Hao Wang, Huaide Xu, Ruidong Wu, Mingzhi Lu, Ke Tongji Univ Sch Automot Studies Shanghai 201800 Peoples R China Tongji Univ Nanchang Automot Inst Intelligence & New Energy Nanchang 330038 Jiangxi Peoples R China

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

关键词： Visual SLAM road constraint graph optimization epipolar constraint plane feature

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Stereo Visual SLAM System with Road Constrained Based on graph optimization 17th

Stereo Visual SLAM System with Road Constrained Based on Gra...

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17th International Conference on Intelligent Robotics and Applications

作者： Zhu, Yuan An, Hao Wang, Huaide Xu, Ruidong Lu, Ke Tongji Univ Sch Automot Studies Shanghai 201800 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In visual SLAM systems for autonomous vehicles, the influence of feature distribution leads to fewer vertical constraints, resulting in significant vertical drift and affecting the long-term localization performance of the system. This paper proposes a graph optimization-based stereo visual SLAM system with road constraints, which enhances vertical constraints by extracting more features from the road and establishing explicit constraints between the vehicle and the road plane. First, a stereo matching method for road feature points is proposed to compensate for the disparity-induced road feature offset between the left and right images, improving the system's ability to extract road features and enhancing feature distribution. Then, the local road plane is used to represent the road, and explicit constraints between the road and the vehicle are established to further increase vertical constraints on the system. Finally, the local road plane, vehicle pose, and map points are optimized jointly as nodes in a nonlinear optimization. Validation through the KITTI dataset and real vehicle experiments shows that the proposed system reduces vertical drift and achieves more accurate localization results.

关键词： Visual SLAM graph optimization Road constrainted

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A Novel INS/USBL Integrated Navigation Scheme via Factor graph optimization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 9239-9249页

作者： Zhang, Liang Hsu, Li-Ta Zhang, Tao Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Techno Minist Educ Nanjing 210096 Peoples R China

Factor graph optimization (FGO) is a well-known approach in the robotics field. Due to multiple iterations and application of large amounts of data, it usually could superior performance than the Kalman Filter (KF) under complex scenarios. However, it is rarely used in the underwater environment, especially for applications based on fiber-optic inertial navigation. Taking the ultra-short baseline system (USBL) as the application background, a novel inertial navigation system (INS)/USBL integrated navigation scheme based on FGO is presented in this paper. The traditional pre-integration factor simplifies the earth rotation and coordinate system, which limits its application in high-precision sensors. This paper proposed an improved pre-integration model using the high precision measurements of fiber optics gyroscope in FGO. Simulation and field tests proved that, with the FGO method, improved accuracy of INS/USBL integration was obtained compared to the traditional KF method. It confirms the potential of FGO in the applications of underwater navigation and positioning.

关键词： Navigation Earth Transponders Inertial navigation Optical fiber filters Mathematical models Kalman filters USBL INS graph optimization integrated navigation pre-integration model

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GON: End-to-end optimization framework for constraint graph optimization problems

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KNOWLEDGE-BASED SYSTEMS 2022年 254卷

作者： Liu, Chuan Wang, Jingwei Cao, Yunkang Liu, Min Shen, Weiming Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Real-world computational applications often require solving combinatorial optimization problems on graphs, i.e., graph optimization problems (GOPs). An emerging trend is using graph neural networks (GNNs) to tackle GOPs. However, for GOPs with constraints, a great challenge faced by GNNs-based methods is to produce optimal solutions that satisfy the constraints. Existing methods relying on supervised learning require a large amount of labeled data, while unsupervised learning methods often require designing elaborate network architectures. To address these limitations, we propose an end-to-end optimization framework for constraint GOPs, coined graph optimization Network (GON). GON is a simple and effective method aiming to address general GOPs without designing specific network architectures for different problems. The proposed framework is evaluated on two typical constraint GOPs, one with only soft constraints (the balanced graph partitioning problem) and the other with hard constraints (the maximum independent set problem). We model the two problems as node assignment problems and design the corresponding constraint-aware loss function for each so that the model can be directly optimized to generate optimal solutions that satisfy the constraints. Experiments on various benchmarks show that the proposed GON equipped with a vanilla GNN can achieve excellent performance on both GOPs, superior to state-of-the-art methods. The results suggest that GON is a promising solver to address NP-hard GOPs.(c) 2022 Elsevier B.V. All rights reserved.

关键词： graph optimization graph neural networks Balanced graph partitioning Maximum independent set

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Some graph optimization problems with weights satisfying linear constraints

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JOURNAL OF COMBINATORIAL optimization 2022年第1期43卷 200-225页

作者： Nip, Kameng Wang, Zhenbo Shi, Tianning Xiamen Univ Sch Math Sci Xiamen Peoples R China Tsinghua Univ Dept Math Sci Beijing Peoples R China

In this paper, we study several graph optimization problems in which the weights of vertices or edges are variables determined by several linear constraints, including maximum matching problem under linear constraints (max-MLC), minimum perfect matching problem under linear constraints (min-PMLC), shortest path problem under linear constraints (SPLC) and vertex cover problem under linear constraints (VCLC). The objective of these problems is to decide the weights that are feasible to the linear constraints, and find the optimal solutions of corresponding graph optimization problems among all feasible choices of weights. We find that these problems are NP-hard and are hard to be approximated in general. These findings suggest us to explore various special cases of them. In particular, we show that when the number of constraints is a fixed constant, all these problems are polynomially solvable. Moreover, if the total number of distinct weights is a fixed constant, then max-MLC, min-PMLC and SPLC are polynomially solvable, and VCLC has a 2-approximation algorithm. In addition, we propose approximation algorithms for various cases of max-MLC.

关键词： graph optimization Linear programming Polynomial-time algorithm Approximation algorithm Computational complexity

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Lidar SLAM Based on Particle Filter and graph optimization for Substation Inspection

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IEEE ACCESS 2022年 10卷 127540-127549页

作者： Tu, Mingyang Zeng, Pingliang Wu, Qiuxuan Jing, Tao Tian, Yangyang Luo, Yanbin Mao, Wandeng Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China State Grid Henan Elect Power Co Elect Power Res Inst Zhengzhou 450018 Peoples R China

Simultaneous Localization and Mapping (SLAM) is the core technology of intelligent substation inspection robot. Because of lightweight computation, Rao-Blackwellized Particle Filter (RBPF) is widely used in two-dimensional SLAM. However, it suffers from poor positioning accuracy, low robustness and rapid cumulative errors despite recent improvement. This paper presents a lidar SLAM system based on RBPF and graph optimization that can adapt to unstructured operating environment of substation. Firstly, the diversity of particles is increased by rebuilding the resample algorithm to improve the robustness of the system, and high-quality poses are estimated in submaps. Secondly, the multi-submap system is established to construct odometry constraints (one pose corresponds to two submaps). Furthermore, loop detector is an important part of optimization algorithm, and the branch-bound method is used to reduce computation burden and accelerate the loop detection. Finally, global poses of robot are optimized by the whole odometry and loop constraints in real time. Experiment results show that the proposed method is more accurate than other methods, and can maintain and produce high-precision positioning and mapping in complex substation operation and maintenance environment. It provides a new idea for intelligent substation inspection and positioning method.

关键词： graph optimization loop closure detection RBPF SLAM substation inspection

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