The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Self-citationPast, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094
Self-citationSE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group
Rosen, D. M., Carlone, L., Bandeira, A. S., & Leonard, J. J. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918784361 · Cited by 303
Self-citationGlobal Structure-from-Motion by Similarity Averaging
Cui, Z., & Tan, P. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.105 · Cited by 160
Very Large-Scale Global SfM by Distributed Motion Averaging
Zhu, S., Zhang, R., Zhou, L., Shen, T., Fang, T., Tan, P., & Quan, L. (2018) · 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.2018.00480 · Cited by 155
Choudhary, S., Carlone, L., Nieto-Granda, C., Rogers, J. G., Christensen, H. I., & Dellaert, F. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917732640 · Cited by 134
Self-citationSimultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis
Zheng, S., Jinling Wang, A., Rizos, C., Ding, W., & El‐Mowafy, A. (2023) · Remote Sensing · doi:10.3390/rs15041156 · Cited by 141
Image-based camera localization: an overview
Wu, Y., Tang, F.-L., & Li, H. (2016) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-018-0008-z · Cited by 114
Distributed Certifiably Correct Pose-Graph Optimization
Tian, Y., Khosoussi, K., Rosen, D. M., & How, J. P. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3072346 · Cited by 97
Rotation Averaging and Strong Duality
Eriksson, A. P., Olsson, C., Kahl, F., & Chin, T.-J. (2018) · 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.2018.00021 · Cited by 96
Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions
Carlone, L., Rosen, D. M., Calafiore, G., Leonard, J. J., & Dellaert, F. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353364 · Cited by 95
Self-citationKim, P., Coltin, B., & Kim, H. J. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2018.8463207 · Cited by 82
Linear RGB-D SLAM for Planar Environments
Kim, P., Coltin, B., & Kim, H. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_21 · Cited by 86
Planar Pose Graph Optimization: Duality, Optimal Solutions, and Verification
Carlone, L., Calafiore, G. C., Tommolillo, C., & Dellaert, F. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2544304 · Cited by 81
Self-citationXu, H., Liu, P., Chen, X., & Shen, S. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3422003 · Cited by 92
Advances in Inference and Representation for Simultaneous Localization and Mapping
Rosen, D. M., Doherty, K., Espinoza, A. T., & Leonard, J. J. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-072720-082553 · Cited by 91
Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age.Preprint
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · arXiv (Cornell University) · Cited by 78
Self-citationAutomated Aerial Triangulation for UAV-Based Mapping
He, F., Zhou, T., Xiong, W., Hasheminasab, S. M., & Habib, A. F. (2018) · Remote Sensing · doi:10.3390/rs10121952 · Cited by 74
Star Tracking Using an Event Camera
Chin, T.-J., Bagchi, S., Eriksson, A. P., & André, V. S. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00208 · Cited by 72
From SLAM to Situational Awareness: Challenges and Survey
Bavle, H., Sánchez-López, J. L., Cimarelli, C., Tourani, A., & Voos, H. (2023) · Sensors · doi:10.3390/s23104849 · Cited by 88
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
Zhang, J. Y., Ramanan, D., & Tulsiani, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_34 · Cited by 77
Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications
Antonante, P., Tzoumas, V., Yang, H., & Carlone, L. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3094984 · Cited by 62
Self-citationShonan Rotation Averaging: Global Optimality by Surfing SO(p) nPreprint
Dellaert, F., Rosen, D. M., Wu, J., Mahony, R. E., & Carlone, L. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 62
Self-citationPapachristos, C., Mascarich, F., Khattak, S., Dang, T., & Alexis, K. (2019) · Autonomous Robots · doi:10.1007/s10514-019-09864-1 · Cited by 62
Cartan-Sync: Fast and Global SE(d)-Synchronization
Briales, J., & González-Jiménez, J. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2718661 · Cited by 56
Learning Transformation Synchronization
Huang, X., Liang, Z., Zhou, X., Xie, Y., Guibas, L. J., & Qi-xing Huang, A. (2019) · 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.2019.00827 · Cited by 57
Visual SLAM: Why Bundle Adjust?
Parra, Á., Chin, T.-J., Eriksson, A. P., & Reid, I. (2019) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2019.8793749 · Cited by 47
Robust synchronization in SO(3) and SE(3) via low-rank and sparse matrix decomposition
Arrigoni, F., Rossi, B., Fragneto, P., & Fusiello, A. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.08.001 · Cited by 53
A Certifiably Correct Algorithm for Synchronization over the Special Euclidean Group
Rosen, D. M., Carlone, L., Bandeira, A. S., & Leonard, J. J. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_5 · Cited by 64
Self-citationNeuRoRA: Neural Robust Rotation Averaging
Purkait, P., Chin, T.-J., & Reid, I. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_9 · Cited by 51
Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework
McGrath, T. M., & Stirling, L. (2020) · Sensors · doi:10.3390/s20236887 · Cited by 49
Cramér–Rao Bounds and Optimal Design Metrics for Pose-Graph SLAM
Chen, Y., Huang, S., Zhao, L., & Dissanayake, G. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3001718 · Cited by 47
Choudhary, S., Carlone, L., Nieto, C., Rogers, J., Christensen, H. I., & Dellaert, F. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487736 · Cited by 51
Self-citationA Survey on Rotation Optimization in Structure from Motion
Tron, R., Zhou, X., & Daniilidis, K. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.133 · Cited by 44
Self-citationBoumal, N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00575 · Cited by 54
Line-Based 6-DoF Object Pose Estimation and Tracking With an Event Camera
Liu, Z., Guan, B., Shang, Y., Yu, Q., & Kneip, L. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3445736 · Cited by 48
Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra
Mangelson, J. G., Ghaffari, M., Vasudevan, R., & Eustice, R. M. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.2994457 · Cited by 41
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Peretroukhin, V., Giamou, M., Nicholas Greene, W., Rosen, D., Kelly, J., & Roy, N. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.007 · Cited by 41
A Comparison of Graph Optimization Approaches for Pose Estimation in SLAM
Jurić, A., Kendes, F., Marković, I., & Petrovic, I. K. (2021) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro52101.2021.9596721 · Cited by 42
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with UncertaintyPreprint
Peretroukhin, V., Giamou, M., Rosen, D. M., Greene, W. N., Roy, N., & Kelly, J. W. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 38
A Robot Web for Distributed Many-Device Localization
Murai, R., Ortiz, J., Sajad Saeedi, G., Kelly, P. H. J., & Davison, A. J. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3324127 · Cited by 43
Brachmann, E., Wynn, J., Chen, S., Cavallari, T., Monszpart, Á., Turmukhambetov, D., & Prisacariu, V. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_24 · Cited by 38
Quantum Permutation Synchronization
Birdal, T., Golyanik, V., Theobalt, C., & Guibas, L. J. (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/cvpr46437.2021.01292 · Cited by 38
Tangent Space Backpropagation for 3D Transformation Groups
Teed, Z., & Deng, J. (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/cvpr46437.2021.01020 · Cited by 37
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
Huang, J., Wang, H., Birdal, T., Sung, M., Arrigoni, F., Hu, S., & Guibas, L. J. (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/cvpr46437.2021.00703 · Cited by 37
SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System
Kim, G., Yun, S., Kim, J., & Kim, A. (2022) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic54506.2022.9748644 · Cited by 37
Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality
Eriksson, A. P., Olsson, C., Kahl, F., & Chin, T.-J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2930051 · Cited by 33
Fan, T., Wang, H., Rubenstein, M., & Murphey, T. D. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3006717 · Cited by 34
Parallel Structure from Motion from Local Increment to Global AveragingPreprint
Zhu, S., Shen, T., Zhou, L., Zhang, R., Wang, J., Fang, T., & Quan, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08601 · Cited by 47
Synchronizing Probability Measures on Rotations via Optimal Transport
Birdal, T., Arbel, M., Şimşekli, U., & Guibas, L. J. (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/cvpr42600.2020.00164 · Cited by 31
A Hierarchical Framework for Collaborative Probabilistic Semantic Mapping
Yue, Y., Zhao, C., Li, R., Yang, C., Zhang, J., Wen, M., Wang, Y., & Wang, D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197261 · Cited by 32
Linear RGB-D SLAM for Structured Environments
Joo, K., Kim, P., Hebert, M., Kweon, I. S., & Kim, H. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3106820 · Cited by 32
Standard Dual Quaternion Optimization and Its Applications in Hand-Eye Calibration and SLAM
Qi, L. (2022) · Communications on Applied Mathematics and Computation · doi:10.1007/s42967-022-00213-1 · Cited by 31
Synchronization Problems in Computer Vision with Closed-Form Solutions
Arrigoni, F., & Fusiello, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01240-x · Cited by 29
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMCPreprint
Birdal, T., Şimşekli, U., Eken, M. O., & Ilić, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12279 · Cited by 30
Fan, T., & Murphey, T. D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341063 · Cited by 29
Visual SLAM With Drift-Free Rotation Estimation in Manhattan World
Liu, J., & Meng, Z.-Y. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3014648 · Cited by 30
Efficient Initial Pose-graph Generation for Global SfM
Baráth, D., Mishkin, D., Eichhardt, I., Shipachev, I., & Matas, J. (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/cvpr46437.2021.01431 · Cited by 29
PoGO-Net: Pose Graph Optimization with Graph Neural Networks
Li, X., & Ling, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00584 · Cited by 26
A General Relationship Between Optimality Criteria and Connectivity Indices for Active Graph-SLAM
Placed, J. A., & Castellanos, J. A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3233230 · Cited by 29
Wang, Z., Qin, K., Zhang, T., & Zhu, B. (2021) · Unmanned Systems · doi:10.1142/s2301385021410065 · Cited by 26
HARA: A Hierarchical Approach for Robust Rotation Averaging
Lee, S. H., & Civera, J. (2022) · 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/cvpr52688.2022.01532 · Cited by 25
Fast global optimality verification in 3D SLAM
Briales, J., & González-Jiménez, J. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759681 · Cited by 26
Sparse Pose Graph Optimization in Cycle Space
Bai, F., Vidal‐Calleja, T., & Grisetti, G. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3050328 · Cited by 27
Fast Autonomous Robotic Exploration Using the Underlying Graph Structure
Placed, J. A., & Castellanos, J. A. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636148 · Cited by 25
Synchronization over Cartan Motion Groups via Contraction
Ozyesil, O., Sharon, N., & Singer, A. (2016) · SIAM Journal on applied algebra and geometry · doi:10.1137/16m1106055 · Cited by 22
Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM
Lenac, K., Ćesić, J., Marković, I., & Petrovic, I. K. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918767756 · Cited by 23
Optimal spatial–temporal triangulation for bearing-only cooperative motion estimation
Zheng, C., Mi, Y., Guo, H., Chen, H., Lin, Z., & Zhao, S. (2025) · Automatica · doi:10.1016/j.automatica.2025.112216 · Cited by 21
Perceptual Aliasing++: Adversarial Attack for Visual SLAM Front-End and Back-End
Ikram, M. H., Khaliq, S., Anjum, M. L., & Hussain, W. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150031 · Cited by 20
Linear RGB-D SLAM for Atlanta World
Joo, K., Oh, T.-H., Rameau, F., Bazin, J., & Kweon, I. S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196561 · Cited by 20
Dihedral Multi-Reference Alignment
Bendory, T., Dan Edidin, B., Leeb, W., & Sharon, N. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3146488 · Cited by 20
Visual SLAM Combining Lines and Structural Regularities: Towards Robust Localization
Zhao, W., Sun, H., Zhang, X., & Xiong, Y. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3311511 · Cited by 20
Scalable Low-Rank Semidefinite Programming for Certifiably Correct Machine Perception
Rosen, D. M. (2021) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-66723-8_33 · Cited by 18
Estimation Contracts for Outlier-Robust Geometric Perception
Carlone, L. (2023) · doi:10.1561/9781638282235 · Cited by 18
Self-citationRobust Incremental Smoothing and Mapping (riSAM)
McGann, D., Rogers, J. G., & Kaess, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161438 · Cited by 19
Majorization Minimization Methods for Distributed Pose Graph Optimization
Fan, T., & Murphey, T. D. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3324818 · Cited by 18
Collaborative Semantic Perception and Relative Localization Based on Map Matching
Yue, Y., Zhao, C., Wen, M., Wu, Z., & Wang, D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340970 · Cited by 18
Augmented Reality Driving Using Semantic Geo-Registration
Chiu, H.-P., Murali, V., Villamil, R., Kessler, G. D., Samarasekera, S., & Kumar, R. (2018) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2018.8447560 · Cited by 18
Drift-correcting self-calibration for visual-inertial SLAM
Nobre, F. D., Kasper, M. E., & Heckman, C. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989771 · Cited by 17
A review of optimisation strategies used in simultaneous localisation and mapping
Huang, S. (2018) · Journal of Control and Decision · doi:10.1080/23307706.2018.1552207 · Cited by 16
To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough
Pitman, M., Hahn, D. F., Tresadern, G., & Mobley, D. L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01579 · Cited by 17
Quasi-Globally Optimal and Real-Time Visual Compass in Manhattan Structured Environments
Kim, P., Li, H., & Joo, K. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3141751 · Cited by 15
A flexible framework for accurate LiDAR odometry, map manipulation, and localization
Blanco, J. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251316881 · Cited by 20
Robust extrinsic calibration of multiple stationary laser range finders
Quenzel, J., Papenberg, N., & Behnke, S. (2016) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2016.7743563 · Cited by 13
Unsupervised monocular visual odometry with decoupled camera pose estimation
Lin, L., Wang, W., Luo, W., Song, L., & Zhou, W. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103052 · Cited by 15
COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots
Yue, Y., Wen, M., Zhao, C., Wang, Y., & Wang, D. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3070497 · Cited by 15
Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM
Doherty, K., Rosen, D. M., & Leonard, J. J. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811788 · Cited by 15
Zhang, Y., Liang, W. G., He, H., & Tan, J. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2795006 · Cited by 15
Parallel Structure from Motion for Sparse Point Cloud Generation in Large-Scale Scenes
Bao, Y., Lin, P., Li, Y., Qi, Y., Wang, Z., Du, W., & Fan, Q. (2021) · Sensors · doi:10.3390/s21113939 · Cited by 14
Efficient and Guaranteed Planar Pose Graph optimization Using the Complex Number Representation
Fan, T., Wang, H., Rubenstein, M., & Murphey, T. D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968044 · Cited by 14
Arrigoni, F., Menapace, W., Benkner, M. S., Ricci, E., & Golyanik, V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_29 · Cited by 13
Certifiable Object Pose Estimation: Foundations, Learning Models, and Self-Training
Talak, R., Peng, L., & Carlone, L. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3271568 · Cited by 15
Self-citationMulti-Sensor SLAM with Online Self-Calibration and Change Detection
Nobre, F. D., Heckman, C., & Sibley, G. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_66 · Cited by 13
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Parra, Á., Chng, S.-F., Chin, T.-J., Eriksson, A. P., & Reid, I. D. (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/cvpr46437.2021.00428 · Cited by 12
Álvarez-Tuñón, O., Brodskiy, Y., & Kayacan, E. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3321032 · Cited by 15
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure
Chng, C.-K., Parra, Á., Chin, T.-J., & Latif, Y. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363388 · Cited by 12
Novel Parameterization for Gauss–Newton Methods in 3-D Pose Graph Optimization
Nasiri, S., Hosseini, R., & Moradi, H. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3034021 · Cited by 14
Fast Pose Graph Optimization via Krylov-Schur and Cholesky Factorization
Moreira, G., Marques, M., & Costeira, J. (2021) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00194 · Cited by 14
Agarwal, S., Shree, V., & Chakravorty, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989746 · Cited by 12
Self-supervised Correspondence Estimation via Multiview Registration
Banani, M. E., Rocco, I., Novotný, D., Vedaldi, A., Neverova, N., Johnson, J., & Graham, B. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00127 · Cited by 11
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
