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Automatic Point Clouds Registration Method Based on Mesh
Segmentation
Lihua Fan1, a, Bo Liu 1, b, Baoling Xie 1, c and Qi Chen2,d 1Shenyang University of Technology, LiaoYang, LiaoNing, 111003, China
2 YiBin Vocational and Technical College, YiBin, SiChuan, 644003, China
Keywords: Point clouds, Registration, Mesh Segmentation
Abstract. This paper proposes an automatic point clouds registration method based on High-Speed
Mesh Segmentation. The proposed method works fast for doing an initial registration and extracting
point clouds region feature. First, the features of the point region are used for matching point cloud
regions. Second, matched regions sets are classified for calculating transform matrix of initial
registration. Based on the initial registration result the Iterative Closest Point (ICP) algorithm which
had been used for accuracy registration to composite point cloud pairs will be applied. The proposed
registration approach is able to do automatic registration without any assumptions about their initial
positions, and avoid the problems of traditional ICP in bad initial estimate. The proposed method plus
with ICP algorithm provides an efficient 3D model for computer-aided engineering and
computer-aided design.
Introduction
In reverse engineering field, the automatic registration for different view point measurement data
has becoming an important research topic. The purpose of the registration is that calculates transform
relationship based on the overlap part from a pair of measurement data of the same object in different
view, and then uses the result for restoring the object. Conventionally, Iterative Closest Point (ICP)
algorithm[1] which proposed by Besl is widely used for registration. However, the ICP algorithm, due
to different initial positions of a pairs of point clouds may cause unstable result. If within limit range
of pairs of point clouds are not able to become close enough, the algorithm is difficult to obtain stable
and right registration result.
On the other hand, references [2,3] based on feature point and feature lines have provided improved
methods for it. However, higher dimension solution has not been considered yet. Thus in this paper a
new method has been proposed and new benefit points of it has been brought.
Proposal
The outline of the propose method is show as follow: Fig.1(a) shown point cloud P and Q obtain
from laser scanner to be registration. By the proposed method, P and Q have been moved to a roughly
registration location as shown in Fig.1(b). Then by ICP algorithm deal with this result to obtain
accuracy registration final result for restore the scanned object.
(a)Initial P (left) and Q(right) point cloud (b)Initial registration result1 (c)Accuracy result2 by ICP
Fig.1 Outline for two point registration
Applied Mechanics and Materials Vols. 423-426 (2013) pp 2587-2590Online available since 2013/Sep/27 at www.scientific.net© (2013) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.423-426.2587
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP,www.ttp.net. (ID: 130.207.50.37, Georgia Tech Library, Atlanta, USA-13/11/14,14:11:54)
Implementation steps of proposal
Main steps. The main steps of the proposed method are shown in Fig.2．
Fig.2 Flow chat of the proposed method
From P and Q point cloud data abstract several point cloud regions. However, the main purpose of
this paper is showing how to align two point clouds using segmented regions, so we assume that Point
cloud data is segmented already. An example of segmentation is shown in Fig.3 .
Fig.3 An example of segmentation
Calculating region features. For each point region, calculate coordinating defined features. We
define two kinds of features, invariant features and variable features. Invariant features that can be
used to find approximate corresponding region do not change even if the point cloud have done rigid
transformation. In this paper, three such next invariant features are calculated.
Eigen-value (λ) of covariance matrix that can be calculated as shown in Eq.(1) and Eq. (2).
∑=
=iN
j
j
i
iq
Nc
1
1
(1)
2588 Applied Materials and Technologies for Modern Manufacturing
∑=
−−=iN
j
T
ijijicqcqC
1
))((
(2)
Where, ci is centre location of the neighbour point set ,and Ci is the correlation matrix. The
eigenvectors {e0,e1,e2} of the correlation matrix Ci i and corresponding Eigen-values {λ0, λ1, λ2}
with λ0 λ1 λ2, can be calculated by Eq.(2)
Average distance (µ) from centre location ci to each point can be calculated as shown in Eq. (3).
∑=
−=iN
j
ji
i
pcN 1
1µ (3)
The average distance(d) from each point pj to the least square fitted plane given by { e0, ci }can be
calculated as shown in Eq.(4).
∑
=
−=jN
i
ij
t
j
cpeN
d1
0 )(1
(4)
Matching regions. By utilizing the feature of the region, all the regions are matched. Through
comparing, the matching regions classification is obtained.
Initial registration: Based on matching regions from one class calculate transformation matrix for
registration point cloud P and Q. And then do initial registration by the matrix.
Accuracy registration. If the initial result satisfy the condition Least Square Error condition of ICP
algorithm, based on the initial registration result apply ICP algorithm to obtain accuracy registration
result.
Result.By the proposal method, three data sets have been given and registration results are shown in
the figures as follows. In one set of point cloud, there are four images, which are input point cloud P,
input point cloud Q, initial registration result and based on initial result applied ICP algorithm
obtained accuracy registration result.
In table.1, the differences between data sets are shown.
Tab.1 Data sets difference
point
distribute edge noise
Set 1 uniform without
Set 2 non-uniform some
Set 3 non-uniform a great
many
The point of the point cloud P,Q in data set 1 are uniform and the edge of the point cloud regions are
without noise.
The point of the point cloud P,Q in data set 2 are non-uniform and the edge of the point cloud
regions are with some noise.
The point of the point cloud P,Q in data set 3 are non-uniform and the edge of the point cloud
regions are with a great many noise.
(a) Point cloud P (b) Point cloud Q
Applied Mechanics and Materials Vols. 423-426 2589
(c) Initial registration result (d) ICP registration result
Fig.5 Data set 2 and registration result
(a) Point cloud P (b) Point cloud Q
(c) Initial registration result (d )ICP registration result
Fig.6 Data set 3 and registration result
Image(c) shows the initial registration result by the propose method. Although following the
increase of edge noise, the initial registration result becoming worse, the result is still good enough for
applying ICP algorithm and avoiding the problem of ICP algorithm might obtain error registration
result due to the initial position of P,Q point cloud.
Conclusion
A new registration method based on feature point clouds region has been proposed in this paper.
Compare with existing method [2, 3] which are based on feature point and feature line, the point cloud
region have ability of anti-noise and obtaining right initial registration result for applying ICP
algorithm in a new point of view in the high dimension.
References
[1] Besl,P.J. and McKay,N.D.. A Method for Registration of 3-D Shapes. IEEE Transactions on
Pattern Analysis and Machine Intelligence, No.14 -2(1992), p.239-256
[2] Fang XU, Xilu ZHAO and Ichiro HAGIWARA. A Study on Automatic Registration in Reverse
Engineering. Transactions of the Japan Society of Mechanical Engineers, Vol.76 (2010), p.
2861-2869
[3] Fang XU, Xilu ZHAO and Ichiro HAGIWARA. Research on High-Speed Automatic
Registration Using Composite-Descriptor-Could-Points(CDCP) Model. Transactions of the
Japan Society of Mechanical Engineers, Vol. 78(2012), p.783-798
2590 Applied Materials and Technologies for Modern Manufacturing
Applied Materials and Technologies for Modern Manufacturing 10.4028/www.scientific.net/AMM.423-426 Automatic Point Clouds Registration Method Based on Mesh Segmentation 10.4028/www.scientific.net/AMM.423-426.2587
