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SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion

Authors: N. Zarrouati, E. Aldea and P. Rouchon, American Control Conference 2012, pp. 4116 - 4123, 27-29 June 2012, Montreal, Canada.
In this paper, we use known camera motion associated to a video sequence of a static scene in order to estimate and incrementally refine the surrounding depth field. We exploit the SO(3)-invariance of brightness and depth fields dynamics to customize standard image processing techniques. Inspired by the Horn-Schunck method, we propose a SO(3)-invariant cost to estimate the depth field. At each time step, this provides a diffusion equation on the unit Riemannian sphere of R3 that is numerically solved to obtain a real time depth field estimation of the entire field of view. Two asymptotic observers are derived from the governing equations of dynamics, respectively based on optical flow and depth estimations: implemented on noisy sequences of synthetic images as well as on real data, they perform a more robust and accurate depth estimation. This approach is complementary to most methods employing state observers for range estimation, which uniquely concern single or isolated feature points.
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BibTeX:
@Proceedings{,
author = {N. Zarrouati, E. Aldea and P. Rouchon},
editor = {},
title = {SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion},
booktitle = {American Control Conference 2012},
volume = {},
publisher = {},
address = {Montreal},
pages = {4116 - 4123},
year = {2012},
abstract = {In this paper, we use known camera motion associated to a video sequence of a static scene in order to estimate and incrementally refine the surrounding depth field. We exploit the SO(3)-invariance of brightness and depth fields dynamics to customize standard image processing techniques. Inspired by the Horn-Schunck method, we propose a SO(3)-invariant cost to estimate the depth field. At each time step, this provides a diffusion equation on the unit Riemannian sphere of R3 that is numerically solved to obtain a real time depth field estimation of the entire field of view. Two asymptotic observers are derived from the governing equations of dynamics, respectively based on optical flow and depth estimations: implemented on noisy sequences of synthetic images as well as on real data, they perform a more robust and accurate depth estimation. This approach is complementary to most methods employing state observers for range estimation, which uniquely concern single or isolated feature points.},
keywords = {Cameras, Convergence, Mathematical model, Observers, Optical imaging, Vectors}}