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Article overview
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Adaptive Continuous Visual Odometry from RGB-D Images | Tzu-Yuan Lin
; William Clark
; Ryan M. Eustice
; Jessy W. Grizzle
; Anthony Bloch
; Maani Ghaffari
; | Date: |
2 Oct 2019 | Abstract: | In this paper, we extend the recently developed continuous visual odometry
framework for RGB-D cameras to an adaptive framework via online hyperparameter
learning. We focus on the case of isotropic kernels with a scalar as the
length-scale. In practice and as expected, the length-scale has remarkable
impacts on the performance of the original framework. Previously it was handled
using a fixed set of conditions within the solver to reduce the length-scale as
the algorithm reaches a local minimum. We automate this process by a greedy
gradient descent step at each iteration to find the next-best length-scale.
Furthermore, to handle failure cases in the gradient descent step where the
gradient is not well-behaved, such as the absence of structure or texture in
the scene, we use a search interval for the length-scale and guide it gradually
toward the smaller values. This latter strategy reverts the adaptive framework
to the original setup. The experimental evaluations using publicly available
RGB-D benchmarks show the proposed adaptive continuous visual odometry
outperforms the original framework and the current state-of-the-art. We also
make the software for the developed algorithm publicly available. | Source: | arXiv, 1910.0713 | Services: | Forum | Review | PDF | Favorites |
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