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.
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State-of-the-art iterative solvers fail to converge to a global minimum of the cost for relatively small noise levels.
“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]
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- 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 › 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
- 3D rotation estimation, pose graph optimization, SLAM, non-convex optimization, robot orientation, convergence rate, robustness
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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.
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