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Kernel-GPA: A globally optimal solution to deformable SLAM in closed-form.
- Source :
- International Journal of Robotics Research; Apr2024, Vol. 43 Issue 4, p456-484, 29p
- Publication Year :
- 2024
-
Abstract
- We study the generalized Procrustes analysis (GPA), as a minimal formulation to the simultaneous localization and mapping (SLAM) problem. We propose Kernel-GPA, a novel global registration technique to solve SLAM in the deformable environment. We propose the concept of deformable transformation which encodes the entangled pose and deformation. We define deformable transformations using a kernel method and show that both the deformable transformations and the environment map can be solved globally in closed-form, up to global scale ambiguities. We solve the scale ambiguities by an optimization formulation that maximizes rigidity. We demonstrate Kernel-GPA using the Gaussian kernel and validate the superiority of Kernel-GPA with various datasets. Code and data are available at https://bitbucket.org/FangBai/deformableprocrustes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02783649
- Volume :
- 43
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- International Journal of Robotics Research
- Publication Type :
- Academic Journal
- Accession number :
- 176065300
- Full Text :
- https://doi.org/10.1177/02783649231195380