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
Nonlinear Constraint Network Optimization for Efficient Map Learning
Grisetti, G., Stachniss, C., & Burgard, W. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026444 · Cited by 222 · cited 1×
“, Levenberg-Marquardt, stochastic gradient descent [2], [3]), or exploiting robust kernels [4].”
BackgroundCombining two-view constraints for motion estimation
Govindu, V. M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990963 · Cited by 196
Global Motion Estimation from Point Matches
Arie-Nachimson, M., Kovalsky, S. Z., Kemelmacher-Shlizerman, I., Singer, A., & Basri, R. (2012) · International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission · doi:10.1109/3dimpvt.2012.46 · Cited by 174
SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion
Crandall, D., Owens, A., Snavely, N., & Huttenlocher, D. P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.218 · Cited by 157
Swarms on Sphere: A Programmable Swarm with Synchronous Behaviors like Oscillator Networks
Olfati‐Saber, R. (2006) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2006.376811 · Cited by 150
Closed-Form Solution of Visual-Inertial Structure from Motion
Martinelli, A. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0647-7 · Cited by 142
Multiview registration of 3D scenes by minimizing error between coordinate frames
Sharp, G. C., Lee, S. W., & Wehe, D. K. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.49 · Cited by 134
Non-sequential structure from motion
Enqvist, O., Kahl, F., & Olsson, C. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130252 · Cited by 130
Riemannian Consensus for Manifolds With Bounded Curvature
Tron, R., Afsari, B., & Vidal, R. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2012.2225533 · Cited by 117
Self-citationDistributed 3-D Localization of Camera Sensor Networks From 2-D Image Measurements
Tron, R., & Vidal, R. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2351912 · Cited by 112
Self-citationOn frame and orientation localization for relative sensing networks
Piovan, G., Shames, I., Fidan, B., Bullo, F., & Anderson, B. D. O. (2013) · Automatica · doi:10.1016/j.automatica.2012.09.014 · Cited by 105
A fast and accurate approximation for planar pose graph optimization
Carlone, L., Aragüés, R., Castellanos, J. A., & Bona, B. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364914523689 · Cited by 101
Self-citationRobustness in Motion Averaging
Govindu, V. M. (2006) · Lecture notes in computer science · doi:10.1007/11612704_46 · Cited by 99
Intrinsic consensus on SO(3) with almost-global convergence
Tron, R., Afsari, B., & Vidal, R. (2012) · 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2012.6426677 · Cited by 94
Self-citationA Linear Approximation for Graph-based Simultaneous Localization and Mapping
Carlone, L., Aragüés, R., Castellanos, J. A., & Bona, B. (2011) · Robotics: Science and Systems · doi:10.15607/rss.2011.vii.006 · Cited by 90
Self-citationEstimator initialization in vision-aided inertial navigation with unknown camera-IMU calibration
Dong-Si, T.-C., & Mourikis, A. I. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386235 · Cited by 89
Carlone, L., & Censi, A. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2291626 · Cited by 79
Self-citationSimultaneous Multiple Rotation Averaging Using Lagrangian Duality
Fredriksson, J., & Olsson, C. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37431-9_19 · Cited by 77
Kneip, L., Weiß, S., & Siegwart, R. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094699 · Cited by 44
A Linear Approximation for Graph-Based Simultaneous Localization and Mapping
Carlone, L., Aragüés, R., Castellanos, J. A., & Bona, B. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9481.003.0011 · Cited by 35
Self-citationAgarwal, P., Grisetti, G., Tipaldi, G. D., Spinello, L., Burgard, W., & Stachniss, C. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907383 · Cited by 29
Vision-based cooperative estimation via multi-agent optimization
Hatanaka, T., Fujita, M., & Bullo, F. (2010) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2010.5717384 · Cited by 21
Efficient trajectory bending with applications to loop closure
Dubbelman, G., Esteban, I., & Klamer Schutte, B. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5652656 · Cited by 19
Sensor Network Localization on the Group of Three-Dimensional Displacements
Peters, J. R., Borra, D., Paden, B. E., & Bullo, F. (2015) · SIAM Journal on Control and Optimization · doi:10.1137/140957743 · Cited by 16
Closed-form Online Pose-chain SLAM
Dubbelman, G., & Brett Browning, A. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631319 · Cited by 14
Orientation only loop-closing with closed-form trajectory bending
Dubbelman, G., Hansen, P., Brett Browning, A., & Dias, M. B. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224760 · Cited by 14
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
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
