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Article overview
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Continuous Direct Sparse Visual Odometry from RGB-D Images | | Maani Ghaffari
; William Clark
; Anthony Bloch
; Ryan M. Eustice
; Jessy W. Grizzle
; | | Date: |
4 Apr 2019 | | Abstract: | This paper reports on a novel formulation and evaluation of visual odometry
from RGB-D images. Assuming a static scene, the developed theoretical framework
generalizes the widely used direct energy formulation (photometric error
minimization) technique for obtaining a rigid body transformation that aligns
two overlapping RGB-D images to a continuous formulation. The continuity is
achieved through functional treatment of the problem and representing the
process models over RGB-D images in a reproducing kernel Hilbert space;
consequently, the registration is not limited to the specific image resolution
and the framework is fully analytical with a closed-form derivation of the
gradient. We solve the problem by maximizing the inner product between two
functions defined over RGB-D images, while the continuous action of the rigid
body motion Lie group is captured through the integration of the flow in the
corresponding Lie algebra. Energy-based approaches have been extremely
successful and the developed framework in this paper shares many of their
desired properties such as the parallel structure on both CPUs and GPUs,
sparsity, semi-dense tracking, avoiding explicit data association which is
computationally expensive, and possible extensions to the simultaneous
localization and mapping frameworks. The evaluations on experimental data and
comparison with the energy-based formulation of the problem confirm the
effectiveness of the proposed technique, especially, when the lack of structure
and texture in the environment is evident. | | Source: | arXiv, 1904.2266 | | Services: | Forum | Review | PDF | Favorites | |
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