iSAM: Incremental Smoothing and Mapping
Open accessAbstract
In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.
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Most SLAM algorithms are based on iterative nonlinear optimization.
“Most SLAM algorithms are based on iterative nonlinear optimization [63, 99, 125, 126, 192, 204].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [126]
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Self-citationiSAM2: Incremental smoothing and mapping using the Bayes tree
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“[22, 31] have proposed a multimodal state estimation framework for planar pushing (similar to two of the three case studies we use in this paper) with factor graphs (iSAM [16]), however they used fiducials for tracking instead of raw RGB data.”
MethodTemporally scalable visual SLAM using a reduced pose graph
Johannsson, H., Kaess, M., Fallon, M., & Leonard, J. J. (2013) · 2013 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2013.6630556 · Cited by 39 · see citing works
Self-citationFast minimum uncertainty search on a graph map representation
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Graph-based terrain relative navigation with optimal landmark database selection
Steiner, T. J., Brady, T. M., & Hoffman, J. A. (2015) · 2015 IEEE Aerospace Conference · doi:10.1109/aero.2015.7119053 · Cited by 16 · see citing works
- Authors
- Kaess, M., Ranganathan, A., & Dellaert, F.
- Affiliations
- Massachusetts Institute of Technology (US)
- Georgia Institute of Technology (US)
- Venue
- IEEE Transactions on Robotics
- Published
- 2008-12
- Volume
- 24
- Issue
- 6
- Pages
- 1365-1378
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2137052305
- Cited by
- 1,076
- References
- 3
- Citations by year
- 2026: 33 · 2025: 53 · 2024: 52 · 2023: 64 · 2022: 64 · 2021: 77 · 2020: 60 · 2019: 68 · 2018: 78 · 2017: 87 · 2016: 65 · 2015: 83 · 2014: 79 · 2013: 70 · 2012: 53
- FWCI
- 61.09
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation (0448111)
- Defense Advanced Research Projects Agency
- 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 › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- ISAM, SLAM, QR decomposition, variable reordering, data association, estimation uncertainty, robot trajectory, loop closure
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