Registration of Multiple Acoustic Range Views for Underwater Scene Reconstruction

doi:10.1006/cviu.2002.0984

Computer Vision and Image Understanding

Volume 87, Issues 1–3, July 2002, Pages 78-89

https://doi.org/10.1006/cviu.2002.0984 Get rights and content

Abstract

This paper proposes a technique for the three-dimensional reconstruction of an underwater environment from multiple acoustic range views acquired by a remotely operated vehicle. The problem is made challenging by the very noisy nature of the data, the low resolution, and the narrow field of view. Our main contribution is a new global registration technique to distribute registration errors evenly across all views. Our approach does not use data points after the first pairwise registration, for it works only on the transformations. Therefore, it is fast and occupies only a small amount of memory. Experimental results suggest the global registration technique is effective in equalizing the error. Moreover, we introduce a statistically sound thresholding (the X84 rejection rule) to improve ICP robustness against noise and nonoverlapping data.

References (27)

Y. Chen et al.
Object modeling by registration of multiple range images
Image Vision Comput.
(1992)
T. Masuda et al.
A robust method for registration and segmentation of multiple range images
Comput. Vision Image Understand.
(1995)
E. Trucco et al.
Robust motion and correspondence of noisy 3-D point sets with missing data
Pattern Recog. Lett.
(1999)
D.W. Eggert et al.
Simultaneous registration of multiple range views for use in reverse engineering of cad models
Comput. Vision Image Understand.
(1998)
R. K. Hansen and P. A. Andersen, A 3-D underwater acoustic camera—properties and applications, in Acoustical ImagingP....
P. Besl et al.
A method for registration of 3-D shapes
IEEE Trans. Pattern Anal. Mach. Intell.
(1992)
Z. Zhang
Iterative point matching of free-form curves and surfaces
Internat. J. Comput. Vision
(1994)
G. Turk et al.
Zippered polygon meshes from range images
F. Hampel et al.
Robust Statistics: The Approach Based on Influence Functions
(1986)
T. Masuda, Generation of geometric model by registration and integration of multiple range images, in International...

H. Jin, T. Duchamp, H. Hoppe, J. A. McDonald, K. Pulli, and W. Stuetzle, Surface reconstruction from misregistered...

R. Benjemaa and F. Schmitt, A solution for the registration of multiple 3D point sets using unit quaternions, in...

R. Bergevin et al.

Towards a general multiview registration technique

IEEE Trans. Pattern Anal. Mach. Intell.

(1996)

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Citation Excerpt :
However, modifications to the original ICP are now widely used to achieve accurate registration even with fairly general starting positions [31]. We adopted an approach similar to the one proposed by Zhang [38], using a modified cost function based on robust statistics to limit the maximum distance between closest points [7]. As pointed out by Zhang [38], the distribution of the residuals for two fully overlapping sets approximates a Gaussian, when the registration is good.
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