Particle filtering for registration of 2D and 3D point sets with stochastic dynamics
Open accessAbstract
In this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in pose parameters obtained from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. Thus, the novelty of our method is twofold: Firstly, we employ a particle filtering scheme to drive the point set registration process. Secondly, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. 1.
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[3] can handle up to 35% noise and the part-in-whole problem.
“These include [3] which can handle up to 35% noise, and part-in-whole problem”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [3]
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Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
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- Authors
- Sandhu, R., Dambreville, S., & Tannenbaum, A.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2008 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2146847221
- Cited by
- 38
- References
- 23
- Citations by year
- 2025: 1 · 2024: 1 · 2021: 3 · 2020: 3 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 8.59
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation
- Brigham and Women's Hospital
- National Institutes of Health (EB005149)
- Multidisciplinary University Research Initiative
- National Institute of Biomedical Imaging and Bioengineering (U54EB005149)
- Air Force Office of Scientific Research
- Army Research Office
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- point cloud registration, particle filter, rigid transformation, stochastic dynamics, iterative closest point, uncertainty modeling, posterior distribution estimation
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