Keywords: 3-D Registration, Unmanned Ground Vehicle, DSM, Post Estimation.

Abstract: In this paper, we propose a new approach which registers a range image which is acquired from a 3-D range

sensor to a DSM to estimate the 3-D pose of an unmanned ground vehicle. Generally, 3-D registration is

divided into two parts that called as coarse and refinement steps. Above all, a proper feature matching

technique is demanded between the DSM and the range image for the coarse registration to register

precisely and speedy. We generated signatures using shape parameterization about the DSM and the range

images and got a 3-D rigid transformation by matching them to minimize registration error.

1 INTRODUCTION

To achieve duty of an UGV(unmanned ground

vehicle), a navigation system is essential to provide

information such as the position of the vehicle,

travelling speed, and etc. However, high accuracy

navigation systems are usually high price so that

additional costs are required for operating them.

Therefore, in order to account for productivity and

an effectiveness of the UGV, a low-priced

navigation system is needed. By the way, solutions

which revise the navigation error are demanded

simultaneously. To overcome this problem, it is

necessary to obtain range images using multiplex

sensors mounted on the UGV and develop a real-

time algorithm which revises the navigation error

using a 3-D registration technique with a pre-

produced DSM(digital surface model).

The 3-D registration technique is to find a rigid

transformation which best fits the data to its

corresponding model, i.e., it is a process which

transforms 3-D geometrical information such as

vertices and surfaces acquired from each private

coordinate system into a common coordinate system.

In general, the 3-D registration techniques are

divided into two parts that one of them is called as

coarse registration which computes an initial

estimation of the rigid motion between two clouds of

points and the other is called as refine registration

which minimizes registration error using the initial

estimation. Hence according to the accuracy of the

initial estimation, the performance of last

registration refinement becomes satisfactory or not.

The key point for the successful coarse

registration is to extract appropriate correspondences

which have reliability between two objects. There

exist a number of the feature extraction methods

which can be grouped in two fundamental

REGISTRATION OF DSM AND RANGE IMAGES FO

R

3-D POSE ESTIMATION OF AN UNMANNED GROUND

VEHICLE

Sung-In Choi, Soon-Yong Park

Department of Computer Engineering, Kyungpook National University

Daegu, 702-701, Republic of Korea

Jaekyoung Moon

School of Electrical Engineering and Computer Science, Kyungpook National University

Daegu, 702-701, Republic of Korea

Jun Kim, Yong-Woon Park

Agency for Defense Development

Yuseong PO Box 35-1, Daejeon 305-600, Republic of Korea

507

Choi S., Park S., Moon J., Kim J. and Park Y. (2009).

REGISTRATION OF DSM AND RANGE IMAGES FOR3-D POSE ESTIMATION OF AN UNMANNED GROUND VEHICLE .

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, pages 507-511

DOI: 10.5220/0001798505070511

Copyright

c

SciTePress

approaches. One of them is 2-D image based

extraction that using corners, edges or chromatic

information and the other is using 3-D geometrical

information such as surface normal and Point

fingerprint (Sun, 2001).

To extract feature points, In (Vandapel, 2006),

Nicolas Vandapel et al. used spin-images for the

terrain model and Soon-Yong Park et al. proposed

SDEBM(sampled depth edge block matching) using

edge information of 3-D model (Park, 2007).

In this paper, we present a new registration

technique which can be applied for the 3-D pose

estimation of an UGV. For the registration, we

generated signatures using shape parameterization

about the DSM and the range images and got a 3-D

rigid transformation by matching them to minimize

registration error.

2 COARSE REGISTRATION BY

MATCHING LOCAL

SIGNATURE

Because our work is an extension of signature based

matching technique, in this section, we review how

signatures are generated and used in previous coarse

registration cases.

2.1 Signature Matching

Generally, the coarse registration between two 3-D

terrain models is accomplished by constructing and

matching signature at selected points on both

surfaces which are invariant by changes in pose.

Correspondences are pair-wised between points with

similar signature and after filtering of the

correspondences, the Euclidean transformation that

registers the two terrain models coarsely is

computed by the correspondences. Here is

noticeable point that to design appropriate signature

which represents invariant characteristic well affects

the matching accuracy.

2.2 Image-based Signature

There are many surface representation techniques

which used for object matching or recognition.

Especially, characterization into a 2-D image by

shape parameterization has been one of the most

popular methods for surface representation. The

spin-images algorithm which introduced by Johnson

and Hebert in (Johnson, 1999) is a typical

application that apply the imaging mechanism to

represent the surface shape. They used two factors

for generating the signature: radial coordinate is a

distance between the central point and a certain

point which is projected into the tangent plane from

a neighboring point x and the elevation coordinate

is the signed perpendicular distance to the tangent

plane. Using these distances, a signature is generated

representing on the x-axis and on the y-axis. As

another well-known image based signature, there is

a surface signature that proposed by Yamany and

Farag in (Yamany, 1999). The main idea of this

approach is to encode the distance and normal

variation between a central point and every other

feature points in the signature. In similar way to the

spin-images, a signature is also generated by

representing the distance and the normal variation on

the x-axis and the y-axis separately.

The main advantage of this image-based signature

matching technique comes from compactness and

stable. Hence it is possible to perform simple and

efficient computation of the similarity of two

surfaces patch by comparing the signatures.

Considering this advantage, in our research, we

aimed at designing a signature that is invariant and

can be computed efficiently in the same manner as

those registration cases.

3 A NEW APPROACH FOR

TERRAIN MAPPING

3.1 KNU Point Signature

Figure 1. shows the fundamental scheme of our

approach. The signature image is generated as

follows: for a central point which is defined by its

3-D coordinates and the normal

, each

neighboring point with its normal

in the

support region can be related by

x

,,

(1)

,

·

,

·

The and are respectively defined as a Euclidean

distance and an inner product of the normals

between central point and each other points and is

defined as a direction angle. Like the spin-image, we

also generated a signature representing on the x-

axis and on y-axis but the bins are filled up with

an accumulation of -value whereas the each bin of

the spin-image contains the number of points that

belong to the corresponding region.

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

508

Figure 1: The fundamental scheme of KNU point signature.

Figure 2: Local terrain characterization.

Since and are in real number space, the

quantization procedure for generating the images is

needed. When the maximum and the minimum value

of and are given, using definition (2), an image

coordinate I can be determined.

,

,

j)(2)

3.2 Feature Selection Strategy

In general, a terrain data consists of millions of

vertices and thus it is not practical to use all of them

as candidate features for matching. Especially points

in flat area have less useful information for

registration. In such regions there is high mutual

similarity and finally, operating the UGV’s point of

view, the facts will affect real-time efficiency of

whole system and matching accuracy. Therefore, it

is necessary to define features take into account the

geometrical attribute of the 3-D shape and the

matching algorithm must be performed between the

features of the objects to efficiently correlate surface

points.

To extract available features, exploring with

respect to former territory of the 3-D model, we took

fractions of regular size and performed evaluation

about flatness, and then the results of the evaluation

were saved into an array which called as feature map.

As shown in the figure 3, there is a NxN patch on the

surface mesh and variance of normals of the vertices

at the patch is computed as follows:

∑

(3)

∑

∑

∑

∑

∑

V

X

,V

Y

,V

Z

) is central point’s 3-D coordinates of

the patch. After measuring the variance, using

definition (4), the patch can be classified as a good

or a bad feature.

(4)

> threshold

Once a classification had been achieved, the result

will be saved into a cell of the feature map. After

this, the feature map will be referenced at run-time

matching.

3.3 Signature Matching

The main purpose of this step is to establish a set of

correspondences between the DSM and the range

data. All signatures have same image size so that the

general template matching method such as SSD(sum

of squared differences), SAD(sum of absolute

distance) and NCC(normalized correlation

coefficient) can be used as a matching solution. For

the matching, we used the NCC. Let P be one of

range data’s signature and Q be one of the DSM’s

signature. Then the correlation coefficient R(P, Q) is

calculated by

,

∑

·

(5)

is the total number of pixels in the domain D

which is defines over the signature image size.

When R is high, the signature P and Q are similar

and when R is low, they are not similar.

In terrain matching scenario, comparing signature

directly without any restriction often drops the real-

time efficiency. Hence, we applied search range as a

constraint to perform the matching algorithm. In this

paper, the search range is specified by 25m in

position of the UGV.

REGISTRATION OF DSM AND RANGE IMAGES FOR3-D POSE ESTIMATION OF AN UNMANNED GROUND

VEHICLE

509

Figure 3: Feature map construction.

4 EXPERIMENTAL RESULT

The experiment was performed with the assumption

that an UGV is moving on the field and a sequence

of range images are obtained continuously from a

range sensor. The range sensor has 45 degree of

field of view and the sensing distance is from 50m to

1km and the range image’s resolution is 128x128. At

this point in time, because of absence of real range

data, we have simulated the scenario using OpenGL.

After estimating initial transformation, for

refinement, we used ICP algorithm (Besl, 1992)

which is widely used for minimizing registration

error. Since original ICP algorithm has time

consuming problem, we applied kd-tree algorithm to

speed-up the ICP and the iteration count for ICP was

selected by 50.

Figure 4 shows the results of the registration

process in order of precedence. Many features are

found at the bush of the ridge and the boundary

between the mountain and the field (fig. 4-a) and

through uniform sampling, the best candidates for

matching were selected (fig. 4-b, 4-c). After

matching process, the correspondences also

established appropriately (fig. 4-d). Finally, we

could notice that the range image was fitted into the

DSM(fig. 4-e).

Table 1. provides timing information and details

on the registration process. As expected, the

matching step keeps the most of registration time.

Table 1: Registration Statistics.

(a) Feature extraction (b) Sampled features of

a range image

(c) Sampled feature of the DSM

(d) A set of correspondences

(e) The result of registration

Figure 4: The total procedure of our approach.

5 CONCLUSIONS

In this paper, we have described registration

technique for 3-D pose estimation of an unmanned

ground vehicle. Using a signature which includes 3-

D geometrical characteristic by shape parameter-

zation, a set of correspondences can be established

and used for the coarse registration.

In future work, we intend to conduct a more

thorough analysis on the registration performance

and through coupling with the 2-D based feature

extraction method, we would like to improve the

registration performance.

ACKNOWLEDGEMENTS

This research has been supported by funding from

the Agency for Defense Development in Korea.

Step Average time (sec)

Feature selection 0.033

Signature generation 0.001

Matching the correspondences 3.157

Initial transformation

estimation

0.489

ICP using kd-tree (50 times

iter.)

0.193

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

510

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Sun, Y., Abidi, M., 2001, Surface matching by 3d point’s

fingerprint, Proc. Of 8

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Vandapel N., Donamukkala, R., R., Hebert, M., 2006, The

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Park, S., Baek, J., 2007. Online Multi-view Image

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Tracking, The 6

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international Conference on 3-D

Digital Imaging and Modeling.

Johnson. A., Hebert, M., 1999. Using spin images for

efficient object recognition in cluttered 3d scenes,

IEEE TPAMI, 21(5): 433-449

Yamany, S., Farag, A., 1999. Free-form surface

registration using surface signatures. Proc. of 7th Int.

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Besl, P., McKay, N. 1992. A method for registration of 3-

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(2): 239-256

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