In this paper, we propose a novel outlier diagnosis method for robust pose estimation of rigid body motions from outlier contaminated 3D point measurements. Due to incorrect correspondences in a cluttered measuring environment, observed point data are contaminated by outliers, which are unusual gross errors that lie out of an overall error distribution. Standard least-squares methods for pose estimation are highly sensitive to outliers. For this reason, an outlier diagnosis method is developed to preprocess measured point data prior to pose estimation. This diagnosis method detects and removes outliers based on a relaxation method with rigid body constraints of a rigid body. Simulations and experiments prove the effectiveness and advantages of high breakdown point and ease of implementation.

References

References
1.
Hodge
,
V. J.
, and
Austin
,
J.
,
2004
, “
A Survey of Outlier Detection Methodologies
,”
Artif. Intell. Rev.
,
22
,
pp.
85
126
. 10.1023/B:AIRE.0000045502.10941.a9
2.
Williams
,
J. A.
,
Bennamoun
,
M.
, and
Latham
,
S.
,
1999
, “
Multiple View 3D Registration: A Review and a New Technique
,”
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC ‘99)
,
Vol.
3
,
pp.
497
502
.
3.
Eggert
,
D. W.
,
Lorusso
,
A.
, and
Fisher
,
R. B.
,
1997
, “
Estimating 3-D Rigid Body Transformations: A Comparison of Four Major Algorithms
,”
Mach. Vision Appl.
,
9
,
pp.
272
290
. 10.1007/s001380050048
4.
Siegel
,
A. F.
, and
Benson
,
R. H.
,
1982
, “
A Robust Comparison of Biological Shapes
,”
Biometrics
,
38
,
pp.
341
350
. 10.2307/2530448
5.
Zhang
,
Z.
,
1994
, “
Iterative Point Matching for Registration of Freeform Curves and Surfaces
,”
Int. J. Comput. Vis.
,
13
(
2
),
pp.
119
152
. 10.1007/BF01427149
6.
Pennec
,
X.
, and
Thirion
,
J. P.
,
1997
, “
A Framework for Uncertainty and Validation of 3-D Registration Methods Based on Points and Frames
,”
Int. J. Comput. Vis.
,
25
(
3
),
pp.
203
229
. 10.1023/A:1007976002485
7.
Zhuang
,
X. H.
, and
Huang
,
Y.
,
1994
, “
Robust 3D-3D Pose Estimation
,”
IEEE Trans. PAMI
,
16
(
8
),
pp.
818
824
. 10.1109/34.308478
8.
Boulanger
,
P.
,
Moron
,
V.
, and
Redarce
,
T.
,
1996
, “
High-Speed and Non-Contact Validation of Rapid Prototyping Parts
,”
Proceedings of the SPIE Rapid Product Development Technologies
,
pp.
46
90
.
9.
Rosin
,
P. L.
,
1999
, “
Robust Pose Estimation
,”
IEEE Trans. Syst., Man, Cybern., Part B: Cybern.
,
29
(
2
),
pp.
297
303
. 10.1109/3477.752804
10.
Enqvist
,
O.
, and
Kahl
,
F.
,
2008
, “
Robust Optimal Pose Estimation
,”
ECCV 2008, Part I, LNCS
,
D.
Forsyth
,
P.
Torr
, and
A.
Zisserman
, eds.,
Springer-Verlag
,
Berlin, Heidelberg
,
Vol.
5302
,
pp.
141
153
.
11.
Cox
,
G. S.
, and
De Jager
,
G.
,
1992
, “
A Survey of Pattern Matching Techniques and a New Approach to Point Pattern Recognition
,”
Proceedings of the IEEE South African Symposium on Communications and Signal Processing
,
pp.
243
248
.
12.
Ranade
,
S.
, and
Rosenfeld
,
A.
,
1980
, “
Point Pattern Matching by Relaxation
,”
Pattern Recogn.
,
12
,
pp.
269
275
. 10.1016/0031-3203(80)90067-9
13.
Rousseeuw
,
P. J.
, and
Leroy
,
A. M.
,
1987
,
Robust Regression and Outlier Detection
,
Wiley
,
New York
.
14.
Lin
,
Y.
,
Tu
,
X.
,
Perron
,
C.
, and
Xi
,
F.
,
2010
, “
A Data Decorrelation Method for 3D Position Measurements
,”
Proceedings of the IEEE/ASME International Conference Advanced Intelligent Mechatronics
,
Montreal
,
pp.
90
95
.
You do not currently have access to this content.