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Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization
Abstract
Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotations are the actual reason why SLAM is a difficult problem, in this work we survey techniques for 3D rotation estimation. Rotation estimation has a rich history in three scientific communities: robotics, computer vision, and control theory. We review relevant contributions across these communities, assess their practical use in the SLAM domain, and benchmark their performance on representative SLAM problems (Fig. 1). We show that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.
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“State-of-the-art iterative solvers fail to converge to a global minimum of the cost for relatively small noise levels [32, 37].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [37]
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Citations
Cites
Distributed Control of Robotic Networks
Bullo, F., Cortés, J., & Martínez, S. (2009) · Princeton University Press eBooks · doi:10.1515/9781400831470 · Cited by 1,538
Distributed Control of Robotic Networks
Bullo, F. (2009) · Cited by 591
Factor Graphs and GTSAM: A Hands-on Introduction
Dellaert, F. (2012) · SMARTech Repository (Georgia Institute of Technology) · Cited by 583
Self-citationHartley, R. I., Trumpf, J., Dai, Y., & Li, H. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0601-0 · Cited by 529
Fast iterative alignment of pose graphs with poor initial estimates
Olson, E., Leonard, J., & Teller, S. (2006) · Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. · doi:10.1109/robot.2006.1642040 · Cited by 427
Robust Rotation and Translation Estimation in Multiview Reconstruction
Martinec, D., & Pajdla, T. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383115 · Cited by 323
Martinelli, A. (2011) · IEEE Transactions on Robotics · doi:10.1109/tro.2011.2160468 · Cited by 283
Consensus Optimization on Manifolds
Sarlette, A., & Sepulchre, R. J. (2009) · SIAM Journal on Control and Optimization · doi:10.1137/060673400 · Cited by 268
Lie-algebraic averaging for globally consistent motion estimation
Govindu, V. M. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315098 · Cited by 256
Efficient and Robust Large-Scale Rotation Averaging
Chatterjee, A., & Govindu, V. M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.70 · Cited by 232
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Items 101–200 of 236
Chordal Based Error Function for 3-D Pose-Graph Optimization
Aloise, I., & Grisetti, G. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2956456 · Cited by 10
Tian, Y., & How, J. P. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3327635 · Cited by 10
Initialization of 3D pose graph optimization using Lagrangian duality
Briales, J., & González-Jiménez, J. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989600 · Cited by 10
A wireless signal-based sensing framework for robotics
Jadhav, N., Wang, W., Zhang, D., Khatib, O., Kumar, S., & Gil, S. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649221097989 · Cited by 10
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed NetworkPreprint
Peretroukhin, V., Wagstaff, B., Giamou, M., & Kelly, J. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03182 · Cited by 10
Instant Motion Tracking and Its Applications to Augmented RealityPreprint
Wei, J., Ye, G., Mullen, T. R., Grundmann, M., Ahmadyan, A., & Hou, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06796 · Cited by 10
Analytical SLAM without linearization
Tan, F., Lohmiller, W., & Slotine, J.-J. E. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917710541 · Cited by 12
Robust Pose-Graph SLAM Using Absolute Orientation Sensing
Agarwal, S., Parunandi, K. S., & Chakravorty, S. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2893436 · Cited by 10
Direct Relative Edge Optimization, A Robust Alternative for Pose Graph Optimization
Jackson, J., Brink, K., Forsgren, B., Wheeler, D. O., & McLain, T. W. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2896478 · Cited by 8
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging
Wilson, K., & Bindel, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00607 · Cited by 9
SE(n)++: An Efficient Solution to Multiple Pose Estimation Problems
Wu, J., Liu, M., Huang, Y., Jin, C., Wu, Y., & Yu, C. B. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3015039 · Cited by 9
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
Cimarelli, C., Bavle, H., Sánchez-López, J. L., & Voos, H. (2022) · Sensors · doi:10.3390/s22072651 · Cited by 9
miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization FrameworkPreprint
Dong, J., & Lv, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00903 · Cited by 9
DecoupledPoseNet: Cascade Decoupled Pose Learning for Unsupervised Camera Ego-Motion Estimation
Zhou, W., Zhang, H., Yan, Z., Wang, W., & Lin, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3144958 · Cited by 8
SCORE: A Second-Order Conic Initialization for Range-Aided SLAM
Papalia, A., Morales, J., Doherty, K., Rosen, D. M., & Leonard, J. J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160787 · Cited by 8
Rotation Synchronization via Deep Matrix Factorization
Tejus, G., Zara, G., Rota, P., Fusiello, A., Ricci, E., & Arrigoni, F. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160548 · Cited by 8
Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM
McGann, D., Lassak, K., & Kaess, M. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611193 · Cited by 8
Analytical SLAM Without Linearization
Tan, F., Lohmiller, W., & Slotine, J.-J. E. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-51532-8_6 · Cited by 8
Compactification of the Rigid Motions Group in Image Processing
Bendory, T., Hadi, I., & Sharon, N. (2022) · SIAM Journal on Imaging Sciences · doi:10.1137/21m1429448 · Cited by 8
Multi-frame Motion Segmentation by Combining Two-Frame Results
Arrigoni, F., Ricci, E., & Pajdla, T. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01544-x · Cited by 8
A Linear Least Square Initialization Method for 3D Pose Graph Optimization Problem
Nasiri, S., Moradi, H., & Hosseini, R. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460741 · Cited by 8
Accurate, scalable and parallel structure from motion
Zhu, S. (2017) · doi:10.14711/thesis-991012532269103412 · Cited by 8
SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware SLAM
Kim, S., Hsiao, R., Nikolić, B., Demmel, J., & Shao, Y. S. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3669940.3707258 · Cited by 7
A Cluster-Based 3D Reconstruction System for Large-Scale Scenes
Li, Y., Qi, Y., Wang, C., & Bao, Y. (2023) · Sensors · doi:10.3390/s23052377 · Cited by 7
Comparison of two different objective functions in 2D point feature SLAM
Wang, H., Huang, S., Yang, G., & Dissanayake, G. (2018) · Automatica · doi:10.1016/j.automatica.2018.08.009 · Cited by 7
MASAT: A fast and robust algorithm for pose-graph initialization
Harsányi, K., Kiss, A., Szirányi, T., & Majdik, A. L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.010 · Cited by 7
HiPE: Hierarchical Initialization for Pose Graphs
Guadagnino, T., Giammarino, L. D., & Grisetti, G. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3125046 · Cited by 8
A Flexible and Efficient Loop Closure Detection Based on Motion Knowledge
Liu, B., Tang, F., Fu, Y., Yang, Y., & Wu, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561126 · Cited by 7
Generalized Proximal Methods for Pose Graph Optimization
Fan, T., & Murphey, T. D. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-95459-8_24 · Cited by 7
GeoD: Consensus-based Geodesic Distributed Pose Graph OptimizationPreprint
Cristofalo, E., Montijano, E., & Schwager, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00156 · Cited by 10
Parallax Bundle Adjustment on Manifold with Improved Global Initialization
Liu, L., Zhang, T. L., Liu, Y., Leighton, B., Zhao, L., Huang, S., & Dissanayake, G. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-44051-0_36 · Cited by 6
Estimation Contracts for Outlier-Robust Geometric Perception
Carlone, L. (2023) · Foundations and Trends in Robotics · doi:10.1561/2300000077 · Cited by 6
Self-citationRotational Outlier Identification in Pose Graphs using Dual Decomposition
Karimian, A., Yang, Z., & Tron, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_24 · Cited by 6
Self-citationIRA++: Distributed Incremental Rotation Averaging
Gao, X., Zhu, L., Cui, H., Xie, Z., & Shen, S. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3118883 · Cited by 6
Algebraically rigorous quaternion framework for the neural network pose estimation problem
Lin, C., Hanson, A. J., & Hanson, S. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01296 · Cited by 9
Non-convex Pose Graph Optimization in SLAM via Proximal Linearized Riemannian ADMM
Chen, X. C. X., Cui, C., Han, D., & Qi, L. (2025) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-025-02759-5 · Cited by 6
Synchronization of Group-labelled Multi-graphs
Cin, A. P. D., Magri, L., Arrigoni, F., Fusiello, A., & Boracchi, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00639 · Cited by 5
Visual-Inertial Odometry Priors for Bundle-Adjusting Neural Radiance Fields
Kim, H., Song, M., Lee, D., & Kim, P. (2022) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas55662.2022.10003959 · Cited by 5
Incremental Cycle Bases for Cycle-Based Pose Graph Optimization
Forsgren, B., Brink, K., Ganesh, P., & McLain, T. W. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3236580 · Cited by 6
RL-PGO: Reinforcement Learning-Based Planar Pose-Graph Optimization
Kourtzanidis, N., & Sajad Saeedi, G. (2023) · IEEE Control Systems Letters · doi:10.1109/lcsys.2023.3340619 · Cited by 5
High-fidelity 3D reconstruction for accurate anatomical measurements in endoscopic sinus surgery
Gunderson, N., Chen, P., Ruthberg, J. S., Bly, R. A., Seibel, E. J., & Abuzeid, W. M. (2025) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.3046391 · Cited by 5
Vision-Based SLAM Navigation for Vibro-Tactile Human-Centered Indoor Guidance
Gulde, T., Kärcher, S. M., & Curio, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_24 · Cited by 5
Shonan Rotation Averaging: Global Optimality by Surfing $$SO(p)^n$$
Dellaert, F., Rosen, D. M., Wu, J., Mahony, R. E., & Carlone, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_18 · Cited by 5
Self-citationIndoor robot localization combining feature clustering with wireless sensor network
Dong, X., Su, B., & Jiang, R. (2018) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1179-1 · Cited by 5
Fast Bayesian graph update for SLAM
Taguchi, S., Deguchi, H., Hirose, N., & Kidono, K. (2022) · Advanced Robotics · doi:10.1080/01691864.2021.2013939 · Cited by 5
Probabilistic Mapping and Navigation: A Survey of Bayesian Meta-Learning for Autonomous Robots
Pal, S., & Nayak, S. K. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02283-8 · Cited by 4
Visual Localization of an Internal Inspection Robot for the Oil-Immersed Transformer
Feng, Y., Kou, Y., & Liu, Y. (2023) · Journal of Robotics · doi:10.1155/2023/6699265 · Cited by 4
Forsgren, B., Kaess, M., Vasudevan, R., McLain, T. W., & Mangelson, J. G. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649241256970 · Cited by 6
Enhancing viewability for first-person videos based on a human perception model
Ma, B., & Reibman, A. R. (2017) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2017.8122223 · Cited by 4
Graph Learning in 4D: A Quaternion-Valued Laplacian to Enhance Spectral GCNs
Fiorini, S., Coniglio, S., Ciavotta, M., & Messina, E. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29088 · Cited by 4
Improved SLAM and Motor Imagery Based Navigation Control of a Mobile Robot
Li, J., Liu, Y., -, W. P. L. J. Z. Y., & Li, Z. (2018) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm.2018.8610763 · Cited by 4
Motion, Dual Quaternion Optimization and Motion Optimization
Qi, L. (2023) · Communications on Applied Mathematics and Computation · doi:10.1007/s42967-023-00262-0 · Cited by 4
Complex-Motion NeRF: Joint Reconstruction and Pose Optimization With Motion and Depth Priors
Kim, H., Lee, D., Kang, S., & Kim, P. (2023) · IEEE Access · doi:10.1109/access.2023.3313184 · Cited by 4
Calafiore, G. C., Carlone, L., & Dellaert, F. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.03437 · Cited by 7
Self-citationGeneralized Proximal Methods for Pose Graph OptimizationPreprint
Fan, T., & Murphey, T. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02709 · Cited by 5
Measuring and Improving the Viewing Experience of First-person Videos
Ma, B., & Reibman, A. R. (2017) · Thematic Workshops of ACM Multimedia · doi:10.1145/3126686.3126748 · Cited by 3
AiSDF: Structure-Aware Neural Signed Distance Fields in Indoor Scenes
Jang, J.-H., Lee, I., Kim, M., & Joo, K. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3375117 · Cited by 3
Ebrahimi, Z., & Deghat, M. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02257-w · Cited by 3
A Deep Learning Architecture for 3D Mapping Urban Landscapes
Rodríguez-Santiago, A. L., Arias-Aguilar, J. A., Takemura, H., & Petrilli-Barceló, A. E. (2021) · Applied Sciences · doi:10.3390/app112311551 · Cited by 3
ODLC_SAM: a novel LiDAR SLAM system towards open-air environments with loop closure
Zhang, J., Wang, S., & Tan, X. (2023) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-07-2023-0145 · Cited by 4
JacobiGPU: GPU-Accelerated Numerical Differentiation for Loop Closure in Visual SLAM
Kumar, D., Gopinath, S., Dantu, K., & Ko, S. Y. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611512 · Cited by 3
Improving Pose Graph Optimization via Efficient Graduated Non-convexity Scheduling
Kang, W., Kim, J., Chung, J., Choi, S., & Kim, T. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-024-0479-y · Cited by 3
An Improved Robot’s Localization and Mapping Method Based on ORB-SLAM
Kuang, H., Wang, X., Liu, X., Ma, X., & Li, R. (2017) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2017.179 · Cited by 3
Relative Constrained SLAM for Robot Navigation
Qian, D., Rahman, S., & Forbes, J. R. (2019) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2019.8814592 · Cited by 3
Research on Robot Positioning Technology Based on Multi Sensor
Yu, F., Yu, H., & Wei, Y. (2019) · International Conference on Computer Network, Electronic and Automation (ICCNEA) · doi:10.1109/iccnea.2019.00094 · Cited by 3
Matrix Difference in Pose-Graph OptimizationPreprint
Aloise, I., & Grisetti, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.00952 · Cited by 3
Zhang, L., Chen, W., Chai, Y., Wang, J., & Zhang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00728 · Cited by 3
WSR: A WiFi Sensor for Collaborative Robotics.Preprint
Jadhav, N., Wang, W., Zhang, D., Khatib, O., Kumar, S., & Gil, S. (2020) · arXiv (Cornell University) · Cited by 3
Raum-Vo: Rotational Adjusted Unsupervised Monocular Visual OdometryPreprint
Cimarelli, C., Bavle, H., Sánchez-López, J. L., & Voos, H. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4032569 · Cited by 3
g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
Qiu, Q., & Lau, M. (2025) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim64088.2025.11175798 · Cited by 2
Pose-Graph-Based UWB SLAM with NLOS Factor Estimation
Hyun, J., & Myung, H. (2022) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas55662.2022.10003893 · Cited by 2
Fit2Ear: Generating Personalized Earplugs from Smartphone Depth Camera Images
Zhao, H., Röddiger, T., Feng, Y., & Beigl, M. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3680525 · Cited by 2
Gravity-Aligned Rotation Averaging with Circular Regression
Pan, L., Pollefeys, M., & Baráth, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_6 · Cited by 2
Efficient Graduated Non-Convexity for Pose Graph Optimization
Kang, W., Kim, J., Chung, J., Choi, S., & Kim, T. (2024) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas63016.2024.10773072 · Cited by 2
A Quasiconvex Formulation for Radial Cameras
Olsson, C., Larsson, V., & Kahl, F. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01434 · Cited by 2
Large-scale DSM registration via motion averaging
Xu, N., & Qin, R. (2024) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-1-2024-275-2024 · Cited by 2
Some Research Questions for SLAM in Deformable Environments
Huang, S., Chen, Y., Zhao, L., Zhang, Y., & Xu, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635883 · Cited by 2
Why Rotation Averaging is Easy.Preprint
Eriksson, A. P., Olsson, C., Kahl, F., Enqvist, O., & Chin, T.-J. (2017) · arXiv (Cornell University) · Cited by 2
Stereo 3D Object Trajectory ReconstructionPreprint
Bullinger, S., Bodensteiner, C., Arens, M., & Stiefelhagen, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09297 · Cited by 2
Advances in Inference and Representation for Simultaneous Localization and MappingPreprint
Rosen, D. M., Doherty, K., Espinoza, A. T., & Leonard, J. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05041 · Cited by 2
IMU-based estimation of human lower body kinematics and applications to extravehicular operations
McGrath, T. M. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2
Loss it right: Euclidean and Riemannian Metrics in Learning-based Visual OdometryPreprint
Álvarez-Tuñón, O., Brodskiy, Y., & Kayacan, E. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.05396 · Cited by 2
Otonom Robotlarla Lokalizasyon ve Nokta Bulutu Tabanlı 3B Haritalama
Açıkel, S., & Gökçen, A. (2019) · European Journal of Science and Technology · doi:10.31590/ejosat.636389 · Cited by 1
An Asynchronous Parallel Algorithm Framework for Decentralized Pose Graph Optimization
Guo, G., & Yi, P. (2023) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr56747.2023.10193900 · Cited by 1
High Accuracy and Robust Robotic Inspection by Constrained Pose Graph Optimization
Wang, Z., Yang, Y., Yan, S., Lin, X., Zhang, X., & Ding, H. (2024) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3515280 · Cited by 1
Wang, Z., Song, Q., Lin, X., Zhang, X., Chen, W., & Yan, S. (2025) · IEEE International Conference on Mechatronics (ICM) · doi:10.1109/icm62621.2025.10934902 · Cited by 1
Weighted multi-node joint refinement for VSLAM pose-graph robustification
Cai, J., Wei, G., & Hu, H. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adcce2 · Cited by 1
Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance
Chaudhuri, R., Deb, S., & Das, A. (2025) · IEEE Access · doi:10.1109/access.2025.3595572 · Cited by 1
Estimating in-plane rotation through log-polar mapped binary joint transform correlator
Pal, A., Monikantan, J., & Nishchal, N. K. (2026) · Journal of Optics · doi:10.1007/s12596-026-03101-6 · Cited by 1
Certifiably Optimal Anisotropic Rotation Averaging
Olsson, C., Lochman, Y., Malmport, J., & Zach, C. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01378 · Cited by 1
Davis, B., Sohre, N., & Guy, S. J. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2858445 · Cited by 1
A Pose-Graph Optimization tool for MATLAB
Soares, J. C. V., & Meggiolaro, M. A. (2018) · Anais do ... Congresso Nacional de Engenharia Mecânica · doi:10.26678/abcm.conem2018.con18-0341 · Cited by 1
Li, K., Cox, J., Noack, B., & Hanebeck, U. D. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.2432 · Cited by 1
Relative Pose Estimation for RGB‐D Human Input Scans via Implicit Function Reconstruction
Liu, P., Yu, T., Zeng, Z., Liu, Y., Zhang, G., & Song, Z. G. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/4351951 · Cited by 1
VICAN: Very Efficient Calibration Algorithm for Large Camera Networks
Moreira, G., Marques, M., Costeira, J. P., & Hauptmann, A. G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611245 · Cited by 1
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Madhavan, R., Fusiello, A., & Arrigoni, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72913-3_2 · Cited by 1
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Sharma, H., Lee, T., Patil, M. J., & Woolsey, C. A. (2020) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc45564.2020.9147775 · Cited by 1
Wang, Y., & Peng, X. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9423-3_39 · Cited by 1
Metadata
- Authors
- Carlone, L., Tron, R., Daniilidis, K., & Dellaert, F.
- Affiliations
- Georgia Institute of Technology (US)
- University of Pennsylvania (US)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 4597-4604
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1536617987
- Cited by
- 237
- References
- 37
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 17 · 2023: 29 · 2022: 24 · 2021: 31 · 2020: 29 · 2019: 22 · 2018: 27 · 2017: 14 · 2016: 11 · 2015: 5
- FWCI
- 32.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- 3D rotation estimation, pose graph optimization, SLAM, non-convex optimization, robot orientation, convergence rate, robustness
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