228
P. Drap et al.
Only a few laboratories worldwide have produced groundbreaking work on optical/acoustic data fusion in an underwater environment. See for example (Singh et al.
2000) and (Fusiello and Murino 2004) where the authors describe the use of techniques that allow the overlaying of photo mosaics on bathymetric 3D digital terrain
maps (Nicosevici et al. 2009). In this case we have important qualitative information
coming from photos, but the geometric definition of the digital terrain map comes
from sonar measurements.
Optical and acoustic surveys can also be merged using structured light and high
frequency sonar as by Chris Roman and his team (Singh et al. 2007). This approach is
very robust and accurate in low visibility conditions but does not carry over qualitative
information.
Merging Cloud of Points Merging between different sources is also one of the
basic problems in computer vision, pattern recognition and robotics.
The fact that the correspondence points between two point clouds are unknown
a priori makes the task of merging difficult and interesting in the same time. These
points of correspondence are useful to estimate the position and orientation of one
point cloud compared to another in the same coordinate system.
The methods used in this topic are often variants of the ICP (Iterative Closest
Point) which has been proposed by Besl and McKay (Besl and McKay 1992) and
which remains the most used in the majority of software for automatic registration
between two models.
This method converges to the first local minimum which is due to the outliers of
the matching. Several solutions have been implemented to solve this problem. Chen
and Medioni (1991) suggest replacing the measurement of distance between points,
which is used in the original method, by measuring the distance between a point and
a tangential plane which makes the algorithm less sensitive to local minima.
Rusinkiewicz and Levoy (2001) provide a comparison between several variants
of the standard algorithm in terms of convergence time. The authors also proposed an
optimized method. The idea behind this method is to classify points in the direction
of their normal, then sampling on each class and reject the outliers.
The objective of these methods is to calculate the rigid transformation between
two partially overlapping point clouds. Their purpose can be decomposed into two
parts; the first part is the matching between points of two clouds. The second part is
the estimation of the transformation 3D.
Assuming that we have a set of matching points: {P i }and
P
i
with i = 1,2...N .
P
i
= R × P i + t
where R is the rotation matrix and t is the translation.
To find the correct transformation between the two point clouds, we must find a
solution that minimizes the least squares error.
err =
N
i=1
RP i + t − P
i
2
P. Drap et al.
Only a few laboratories worldwide have produced groundbreaking work on optical/acoustic data fusion in an underwater environment. See for example (Singh et al.
2000) and (Fusiello and Murino 2004) where the authors describe the use of techniques that allow the overlaying of photo mosaics on bathymetric 3D digital terrain
maps (Nicosevici et al. 2009). In this case we have important qualitative information
coming from photos, but the geometric definition of the digital terrain map comes
from sonar measurements.
Optical and acoustic surveys can also be merged using structured light and high
frequency sonar as by Chris Roman and his team (Singh et al. 2007). This approach is
very robust and accurate in low visibility conditions but does not carry over qualitative
information.
Merging Cloud of Points Merging between different sources is also one of the
basic problems in computer vision, pattern recognition and robotics.
The fact that the correspondence points between two point clouds are unknown
a priori makes the task of merging difficult and interesting in the same time. These
points of correspondence are useful to estimate the position and orientation of one
point cloud compared to another in the same coordinate system.
The methods used in this topic are often variants of the ICP (Iterative Closest
Point) which has been proposed by Besl and McKay (Besl and McKay 1992) and
which remains the most used in the majority of software for automatic registration
between two models.
This method converges to the first local minimum which is due to the outliers of
the matching. Several solutions have been implemented to solve this problem. Chen
and Medioni (1991) suggest replacing the measurement of distance between points,
which is used in the original method, by measuring the distance between a point and
a tangential plane which makes the algorithm less sensitive to local minima.
Rusinkiewicz and Levoy (2001) provide a comparison between several variants
of the standard algorithm in terms of convergence time. The authors also proposed an
optimized method. The idea behind this method is to classify points in the direction
of their normal, then sampling on each class and reject the outliers.
The objective of these methods is to calculate the rigid transformation between
two partially overlapping point clouds. Their purpose can be decomposed into two
parts; the first part is the matching between points of two clouds. The second part is
the estimation of the transformation 3D.
Assuming that we have a set of matching points: {P i }and
P
i
with i = 1,2...N .
P
i
= R × P i + t
where R is the rotation matrix and t is the translation.
To find the correct transformation between the two point clouds, we must find a
solution that minimizes the least squares error.
err =
N
i=1
RP i + t − P
i
2