Consistent sparsification for graph optimization
Abstract
In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: (i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) - which is conveyed in the discarded measurements - about the remaining nodes after marginalization; and (ii) sparsifying edges by formulating and solving a consistent ℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.
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Huang et al. sparsify the Hessian matrix by solving an l1-regularized minimization problem.
“Huang et al. [107] sparsify the Hessian matrix (arising in the normal equations) by solving an ℓ 1 -regularized minimization problem.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [107]
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Cites
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346 · see citing works
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988 · see citing works
iSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362 · see citing works
Self-citationStrasdat, H., Montiel, J. M. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.009 · Cited by 439 · cited 1× · see citing works
“Graph-based batch optimization methods for SLAM recently have prevailed (e.g., [1], [3], [4], [17]–[23]).”
MethodFast 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 · see citing works
Self-citationNonlinear 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 · see citing works
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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 · see citing works
Self-citationCommunication-constrained multi-AUV cooperative SLAM
Paull, L., Huang, G., Seto, M., & Leonard, J. J. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139227 · Cited by 116 · see citing works
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- Authors
- Huang, G., Kaess, M., & Leonard, J. J.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Venue
- 2013 European Conference on Mobile Robots
- Publisher
- IEEE
- Published
- 2013-09
- Pages
- 150-157
- OpenAlex
- W2033923716
- Cited by
- 70
- References
- 6
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 3 · 2020: 2 · 2019: 7 · 2018: 6 · 2017: 8 · 2016: 9 · 2015: 11 · 2014: 6 · 2013: 1
- FWCI
- 15.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- European Conference on Mobile Robots (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- graph sparsification, pose graph, SLAM, graph optimization
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