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Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第3期17卷

作者： Feng, Youyang Wang, Qing Yang, Gaochao Southeast Univ Instrumental Sci & Engn Nanjing 210000 Jiangsu Peoples R China

pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universitat Munchen (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures;Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

关键词： pose graph optimization computer vision visual simultaneous localization and mapping optimization method

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Improved pose graph optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions 21st

Improved Pose Graph Optimization for Planar Motions Using Ri...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Li, Kailai Cox, Johannes Noack, Benjamin Hanebeck, Uwe D. Karlsruhe Inst Technol KIT Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot Karlsruhe Germany

We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaternions representing planar motions, the nonlinear structure of the SE(2) group is inherently considered. To solve the on-manifold least squares problem, a Riemannian Gauss-Newton method using the exponential retraction is applied. The proposed Riemannian pose graph optimizer (RPG-Opt) is further evaluated based on public planar pose graph data sets. Compared with state-of-the-art frameworks, the proposed method gives equivalent accuracy and better convergence robustness under large uncertainties of odometry measurements. Copyright (C) 2020 The Authors.

关键词： Parameter estimation Riemannian geometry manifold optimization planar rigid body motion pose graph optimization

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Improved pose graph optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions

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IFAC-PapersOnLine 2020年第2期53卷 9541-9547页

作者： Kailai Li Johannes Cox Benjamin Noack Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaternions representing planar motions, the nonlinear structure of the SE(2) group is inherently considered. To solve the on-manifold least squares problem, a Riemannian Gauss– Newton method using the exponential retraction is applied. The proposed Riemannian pose graph optimizer (RPG-Opt) is further evaluated based on public planar pose graph data sets. Compared with state-of-the-art frameworks, the proposed method gives equivalent accuracy and better convergence robustness under large uncertainties of odometry measurements.

关键词： Parameter estimation Riemannian geometry manifold optimization planar rigid body motion pose graph optimization

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A Linear Least Square Initialization Method for 3D pose graph optimization Problem

A Linear Least Square Initialization Method for 3D Pose Grap...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Nasiri, S. M. Moradi, H. Hosseini, R. Univ Tehran Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9781538630815

pose graph optimization (PGO) is an important optimization problem arising in robotics and machine vision applications like 3D reconstruction and 3D SLAM. Each node of pose graph corresponds to an orientation and a location. The PGO problem finds orientations and locations of the nodes from relative noisy observation between nodes. Recent investigations show that well-known iterative PGO solvers need good initialization to converge to good solutions. However, we observed that state-of-the-art initialization methods obtain good initialization only in low noise problems, and they fail in challenging problems having more measurement noise. Consequently, iterative methods may converge to bad solutions in high noise problems. In this paper, a new method for obtaining orientations in the PGO optimization problem is presented. Like other well-known methods the initial locations are obtained from the result of a least-squares problem. The proposed method iteratively approximates the problem around current estimation and converts it to a least-squares problem. Therefore, the method can be seen as an iterative least-squares method which is computationally efficient. Simulation results show that the proposed initialization method helps the most well-known iterative solver to obtain better optima and significantly outperform other solvers in some cases.

关键词： pose graph optimization Least square 3D SLAM Initialization method

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A Linear Least Square Initialization Method for 3D pose graph optimization Problem

A Linear Least Square Initialization Method for 3D Pose Grap...

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IEEE International Conference on Robotics and Automation

作者： S. M. Nasiri H. Moradi R. Hosseini School of Electrical and Computer Engineering University of Tehran Tehran Iran

关键词： pose graph optimization Least square 3D SLAM Initialization method

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From Angular Manifolds to the Integer Lattice: Guaranteed Orientation Estimation With Application to pose graph optimization

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IEEE TRANSACTIONS ON ROBOTICS 2014年第2期30卷 475-492页

作者： Carlone, Luca Censi, Andrea Georgia Inst Technol Coll Comp Atlanta GA 30332 USA MIT Informat & Decis Syst Lab Cambridge MA 02139 USA

pose graph optimization from relative measurements is challenging because of the angular component of the poses: the variables live on a manifold product with nontrivial topology and the likelihood function is nonconvex and has many local minima. Because of these issues, iterative solvers are not robust to large amounts of noise. This paper describes a global estimation method, called multi-hypothesis orientation-from-lattice estimation in 2-D (MOLE2D), for the estimation of the nodes' orientation in a pose graph. We demonstrate that the original nonlinear optimization problem on the manifold product is equivalent to an unconstrained quadratic optimization problem on the integer lattice. Exploiting this insight, we show that, in general, the maximum likelihood estimate alone cannot be considered a reliable estimator. Therefore, MOLE2D returns a set of point estimates, for which we can derive precise probabilistic guarantees. Experiments show that the method is able to tolerate extreme amounts of noise, far above all noise levels of sensors used in applications. Using MOLE2D's output to bootstrap the initial guess of iterative pose graph optimization methods improves their robustness and makes them avoid local minima even for high levels of noise.

关键词： Integer quadratic programming mobile robots multi-hypothesis estimation orientation estimation pose graph optimization simultaneous localization and mapping (SLAM) SO(2) manifold

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A fast and accurate approximation for planar pose graph optimization

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2014年第7期33卷 965-987页

作者： Carlone, Luca Aragues, Rosario Castellanos, Jose A. Bona, Basilio Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Clermont Univ Inst Pascal Clermont Ferrand France CNRS Aubiere France Univ Zaragoza Dept Informat & Ingn Sistemas Inst Invest Ingn Aragon Zaragoza Spain Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

This work investigates the pose graph optimization problem, which arises in maximum likelihood approaches to simultaneous localization and mapping (SLAM). State-of-the-art approaches have been demonstrated to be very efficient in medium- and large-sized scenarios;however, their convergence to the maximum likelihood estimate heavily relies on the quality of the initial guess. We show that, in planar scenarios, pose graph optimization has a very peculiar structure. The problem of estimating robot orientations from relative orientation measurements is a quadratic optimization problem (after computing suitable regularization terms);moreover, given robot orientations, the overall optimization problem becomes quadratic. We exploit these observations to design an approximation of the maximum likelihood estimate, which does not require the availability of an initial guess. The approximation, named LAGO (Linear Approximation for pose graph optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima. We provide analytical results on existence and sub-optimality of LAGO, and we discuss the factors influencing its quality. Experimental results demonstrate that LAGO is accurate in common SLAM problems. Moreover, it is remarkably faster than state-of-the-art techniques, and is able to solve very large-scale problems in a few seconds.

关键词： pose graph optimization simultaneous localization and mapping mobile robots graph theory linear estimation

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A large-scale multiview point cloud registration method based on distance statistical distribution of weak features

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第1期12卷 312-330页

作者： Feng, Yun Tao, Guoren Wu, Wenlei Lin, Jiahao Liu, Xiaojun Chen, Liangzhou Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430000 Hubei Peoples R China Guilin Measuring & Cutting Tool Co Ltd Guilin 541000 Guangxi Peoples R China

There are issues such as poor layered smoothness, model distortion, and error accumulation in the process of large-scale weak feature point cloud stitching and registration. This paper proposes a precise point cloud registration method based on distance statistical distribution. By summarizing and statistically analyzing the distance thresholds during the iterative process, it accurately determines the closest points for point cloud registration, thus avoiding the problem of low registration accuracy caused by manually setting distance thresholds. By statistically analyzing the distance intervals of corresponding points in the point cloud to eliminate erroneous correspondences and utilizing pose graph optimization for global pose, this method ensures the smoothness and accuracy of point cloud registration. Experiments have validated the effectiveness of this method. Comparative experiments have demonstrated that this method surpasses traditional point cloud registration methods in terms of accuracy, convergence, and robustness.

关键词： Point cloud registration distance statistics pose graph optimization

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Weighted multi-node joint refinement for VSLAM pose-graph robustification

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第5期36卷 056303-056303页

作者： Cai, Jie Wei, Guoliang Hu, Hongbo Univ Shanghai Sci & Technol Coll Sci Jungong Rd Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Business Sch Jungong Rd Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Jungong Rd Shanghai 200093 Peoples R China

In visual simultaneous localization and mapping (VSLAM) systems, pose-graph optimization accuracy is often compromised by noise and errors in loop closures, especially in large-scale or complex environments. This paper introduces an intermediate processor to address these challenges and proposes a novel algorithm, Weighted Multi-Node Joint Refinement for VSLAM pose-graph Robustification (WMJR-PGO). The WMJR-PGO algorithm incorporates a spatial residual mapping test to enhance loop closure detection and a weighted breadth-first search to refine pose estimation. This approach effectively mitigates front-end errors and ensures back-end consistency. Extensive experiments show that the WMJR-PGO algorithm achieves significant improvements across various noise levels. On the Tours dataset, WMJR-PGO outperforms the RS algorithm by 63 % in optimization accuracy under a noise standard deviation of sigma r=0.5. Furthermore, the WMJR-PGO algorithm demonstrates superior performance on multiple datasets even with a noise standard deviation of sigma r=0.5, proving its robustness in noisy environments. The proposed intermediate processor serves as an independent optimization module and provides efficient initialization for iterative optimization methods, accelerating convergence and improving overall system performance.

关键词： VSLAM pose graph optimization spatial residual mapping weighted breadth-first-search

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A Binocular Vision/IMU MSCKF Localization Method Considering Dynamic Initialization and Loopback Detection

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IEEE SENSORS JOURNAL 2025年第1期25卷 1286-1303页

作者： Li, Yandong Liu, Fei Zhang, Jixian Wang, Jian Han, Houzeng Hao, Chunting Beijing Univ Civil Engn & Architecture Sch Geomatics & Urban Spatial Informat Beijing 102616 Peoples R China Moganshan Geospatial Informat Lab Huzhou 313299 Zhejiang Peoples R China

Addressing the issue of excessive error accumulation in visual-inertial odometry under complex and adverse environments, leading to degraded positioning accuracy or even failure, this article proposes a dual-camera visual/inertial measurement unit (IMU) multistate constraint Kalman filter (MSCKF) positioning methodology that incorporates dynamic initialization and loop closure detection, drawing inspiration from the visual-inertial system-monocular (VINS-Mono) algorithmic structure. In the initial phase, to overcome the limitation of conventional MSCKF algorithms that can only perform static initialization, thereby failing in motion estimation under initial dynamic conditions, we augment the MSCKF framework with a dynamic initialization module, furnishing the visual-inertial odometry (VIO) system with a robust initial state. In the backend, we introduce a pose graph optimization and loop closure detection module to enhance positioning accuracy and robustness in complex and harsh environments. Comparative precision experiments were conducted between the proposed algorithm, the original msckf_vio, the monocular visual-inertial vins_mono, and the stereo visual-inertial vins_fusion, utilizing both the EuRoc open-source datasets and real-world scenarios. The experimental results demonstrate that the proposed algorithm effectively provides accurate initial system states and outperforms both VINS-Mono and VINS-Fusion in terms of positioning accuracy. Practical tests further confirm the algorithm's ability to significantly improve positioning precision and stability under challenging conditions such as complex lighting variations and sparse textures.

关键词： Accuracy Visualization Heuristic algorithms Sensors Location awareness Feature extraction Lighting Simultaneous localization and mapping optimization Robustness Dynamic initialization loopback detection pose graph optimization simultaneous localization and mapping (SLAM) visual-inertial odometry (VIO)

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