Hierarchical optimization on manifolds for online 2D and 3D mapping
Abstract
In this paper, we present a new hierarchical optimization solution to the graph-based simultaneous localization and mapping (SLAM) problem. During online mapping, the approach corrects only the coarse structure of the scene and not the overall map. In this way, only updates for the parts of the map that need to be considered for making data associations are carried out. The hierarchical approach provides accurate non-linear map estimates while being highly efficient. Our error minimization approach exploits the manifold structure of the underlying space. In this way, it avoids singularities in the state space parameterization. The overall approach is accurate, efficient, designed for online operation, overcomes singularities, provides a hierarchical representation, and outperforms a series of state-of-the-art methods.
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Grisetti et al. propose a hierarchy of submaps: whenever an observation is acquired, the highest level of the hierarchy is modified and only the areas which are substantially affected are changed at lower levels.
“Grisetti et al. [98] propose a hierarchy of submaps: whenever an observation is acquired, the highest level of the hierarchy is modified and only the areas which are substantially affected are changed at lower levels.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [98]
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Cites
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 · see citing works
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 7× · see citing works
“[ms] Our Approach TreeMap [5] TORO [9]”
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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
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
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885 · see citing works
Multiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324 · see citing works
Robust loop closing over time for pose graph SLAM
Latif, Y., Cadena, C., & Neira, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913498910 · Cited by 221 · see citing works
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175 · see citing works
An optimization-based approach to human body motion capture using inertial sensors
Kok, M., Hol, J. D., & Schön, T. B. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.02252 · Cited by 131 · see citing works
Temporally 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
- Authors
- Grisetti, G., Kummerle, R., Stachniss, C., Frese, U., & Hertzberg, C.
- Affiliations
- University of Freiburg (DE)
- University of Bremen (DE)
- Venue
- 2010 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 273-278
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2029780423
- Cited by
- 199
- References
- 2
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 12 · 2020: 7 · 2019: 8 · 2018: 13 · 2017: 11 · 2016: 13 · 2015: 20 · 2014: 22 · 2013: 22 · 2012: 23
- FWCI
- 41.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- 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 › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- hierarchical optimization, graph-based SLAM, web mapping, manifold structure, data association, robot localization
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