REAL-TIME LOCALIZATION OF AN UNMANNED GROUND

VEHICLE USING A 360 DEGREE RANGE SENSOR

Soon-Yong Park and Sung-In Choi

School of Computer Science and Engineering, Kyungpook National University

1370 Sankyuk-dong, Puk-gu, Daegu, 702-701 Korea

Keywords:

Localization, 3D Sensor, Registration, Unmanned vehicle.

Abstract:

A computer vision technique for the localization of an unmanned ground vihicle (UGV) is presented. The pro-

posed technique is based on 3D registration of a sequence of 360 degree range data and a digital surface model

(DSM). 3D registration of a sequence of dense range data requires a large computation time. For real time

localization, we propose projection-based registration and uniform arc length sampling (UALS) techniques.

UALS reduces the number of 3D sample points while maintaining their uniformity over range data in terms

of ground sample distance. The projection-based registration technique reduces the time of 3D correspon-

dence search. Experimental results from two real navigation paths are shown to verify the performance of the

proposed method.

1 INTRODUCTION

Three dimensional (3D) registration is a computer vi-

sion technique to align multi-view range data with re-

spect to a common coordinate system. Many inves-

tigations have been introduced for 3D model recon-

struction, 3D robot vision, etc. Recently in robotics

community, 3D registration is applied to localization

of unmanned robots or vehicles from range data ac-

quired from 3D sensors.

A 3D sensor mounted on an unmanned vehicle

captures the 3D shape around the vehicle, which is

a local 3D map represented with respect to the sensor

coordinate system. If there is a global and reference

3D map which contains the 3D shape information of

navigation environment, the vehicle location can be

determined by matching the local map with respect to

the global map. By the way, an initial position of the

vehicle can be coarsely estimated by a GPS or INS

sensor. Therefore, it only needs to reﬁne the initial

position to correctly match local and global 3D maps.

A common approach of 3D registration is using

the ICP algorithm (Besl and McKay., 1992). R. Mad-

havan et. al. (Madhavan et al., 2005) register a se-

quence of 3D range data in a pair-wise manner to de-

termine a robot pose. A modiﬁed ICP algorithm is

employed to cope with matching outliers. Triebel et.

al. (Triebel et al., 2006) introduce multi-level surface

maps to classify surface patches to several object cat-

egories. Levinson et. al. (Levinson et al., 2007) use a

digital map of urban environment. A particle ﬁlter is

used to match local range data to the map.

In a few investigations, 360 degree range sensors

are used to capture omnidirectional range data. Him-

melsbach et. al. (Himmelsbach et al., 2008) segment,

classify, and track 3D objects using a 360 degree laser

sensor. They generate occupancy grids from range

data to identify obstacles. K¨ummerle et. al. (Kum-

merle et al., 2009) present an autonomous driving

technique of an unmanned vehicle which is equipped

with multiple navigation sensors including a 360 de-

gree range sensor. In our previous work, a 3D reg-

istration technique is introduced to align 360 degree

range data and a digital surface model (DSM)(Park

and Choi, 2009).

Matching 3D maps obtained from different coor-

dinate systems requires a reasonable number of cor-

respondences between the maps (Hartley and Zisser-

man, 2000). Since a 360 degree range sensor cap-

tures a huge number of 3D points, it requires a sig-

niﬁcant time for 3D registration. For real-time lo-

calization of an unmanned ground vehicle (UGV),

we introduce projection-based registration and uni-

form ground distance sampling techniques to reduce

the number of correspondence. Experimental results

show that the proposed method can ﬁnd robot position

in about 15Hz rate.

610

Park S. and Choi S..

REAL-TIME LOCALIZATION OF AN UNMANNED GROUND VEHICLE USING A 360 DEGREE RANGE SENSOR.

DOI: 10.5220/0003362406100613

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2011), pages 610-613

ISBN: 978-989-8425-47-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 RANGE FORMAT & SAMPLING

2.1 Range Data Format

The range sensor used in this paper is a Velodyne

HDL-64ES2 LADAR sensor mounted on the top of

an UGV. The sensor captures 360 degree range data

by rotating 64 lasers and detectors at 10Hz revolution

rate. The sensor is mounted at 2.081m height from

the ground. The vertical ﬁeld of view of the sensor

is 26.8 degree, 2 degree above and 24.2 degree below

the horizontal plane.

Each laser detector captures maximum 2048 3D

points in a single revolution. Total 64 laser detectors

then capture 3D point clouds which are represented

with respect to the sensor coordinate system as shown

in Figure 1. To rasterize the 3-D data, we convert

the point clouds to a 2.5D depth map called “range

frame”. The size of the map is 64× 1800 in row and

column, where the row index L and the column index

φ correspond to the laser number and the horizontal

angle, respectively. As the column index, 360 degree

range data is quantized with 0.2 degree angular reso-

lution.

Figure 1: Format of 360 degree range frame.

2.2 Uniform Arc Length Sampling

In this section, we propose a new sampling method

called a uniform arc length sampling (UALS) tech-

nique. In contrast to conventional uniform point sam-

pling (UPS) techniques, our approach uniformly sam-

ples 3D points in terms of ground distance along the

scan circles. Let α(L) be the circumference of a scan-

ning circle of a line L, and θ(L) be a vertical an-

gle between the horizontal plane and a ray from the

sensor to line L. Then α(L) is proportional to the

tan(90− θ(L)).

If θ(L) is larger than 1.0 degree, α(L) is com-

puted as shown in Equation 1. In this equation, h is

the height of the sensor from the ground, d

max

is the

maximum sensing distance, and α

max

is the maximum

circumference. The sampling interval φ

(L) along the

rotation angle is then computed by dividing the max-

imum sensing circumference by α(L) as in Equation

α(L) =



max

= 2πd

max

if θ > 1.0,

2πh× tan(90− θ(L)) otherwise.

(1)

(L) = ⌊

kα

max

α(L)

+ 0.5⌋ (2)

In each scanning line of a range frame, we sam-

ple 3D points from the ﬁrst point P(0, L) to the i-th

sample point such that

(L) = P(i× φ

(L), L). (3)

As shown in the right of Figure 2, sampling is done

with uniform arc length kα

max

regardless of scanning

distance of each line. Here, k is a constant to con-

trol the density of sampled points. However, in con-

ventional UPS method, φ

(L) is constant. Depending

on the value of φ

(L), the sampling density could be

too dense or sparse in UPS method. On the contrary,

UALS method samples 3D points uniformly over all

scanning lines in terms of ground sample distance.

Figure 2: Comparison of two sampling methods, UPS and

UALS.

3 AUTOMATIC LOCALIZATION

3.1 Localization Scheme

Localization of an UGV is done by two separate regis-

tration steps as shown in Figure 3. A pairwise (local)

registration step is followed by a (global) registration

step. The local registration aligns two consecutive

range frames and computes the transformation of the

current range frame with respect to an initial position.

However, accumulation errors due to the registration

of a long sequence of range data could yield erroneous

localization. To overcome this problem, a global reg-

istration is done next (Park and Choi, 2009).

Figure 3: Flow of autonomous localization.

3.2 Projection-based ICP

In pair-wise registration, it is required to ﬁnd corre-

REAL-TIME LOCALIZATION OF AN UNMANNED GROUND VEHICLE USING A 360 DEGREE RANGE SENSOR

611

spondences between two 3D range frames, source and

destination as shown in Figure 4. Suppose there is a

3D point P in the source frame. Then, we need to ﬁnd

a matching point Q

′

in the destination. A projection-

based registration is introduced for fast correspon-

dence search.

Suppose the source is the current frame (n) and

the destination is the previous frame (n-1). The pre-

vious frame is considered to be aligned already with

respect to a reference coordinate sytem. Let the cur-

rent position of the source frame be the same with that

of the destination, which is T

n−1,0

. To ﬁnd the corre-

spondence, we ﬁrst transform the source point P to

the initial position such that P

′

= T

−1

n−1,0

P. Second,

′

is projected to a 2D point q(φ, L) in the destina-

tion frame by calculating φ and L. Then, we search Q

which is the closest to P. Since two range frames are

reasonably close enough, we can restrict the search

range as shown in the ﬁgure. In real experiments, we

ﬁx the angle search range to ±15

◦

and the line range

to ±3 from q(φ, L).

The corresponding point Q

′

is now derived as the

intersection of the normal vector of P with the tangent

plane at Q. After a sufﬁcient number of correspon-

dences are deﬁned, a transformation matrix T

pair

] can be derived to minimizes the registra-

tion error ε

pair

as in Equation 4.

pair

∑

k=1

′

− (R

pair

+ t

pair

. (4)

After the pair-wise registration is ﬁnished, the posi-

tion of the current frame is reﬁned using a digital sur-

face model. An ICP-based method is used in the sec-

ond step. However, the DSM is divided into multi-

ple elevation layers to speed up the global registration

(Park and Choi, 2009).

Figure 4: Projection-based pairwise registration scheme.

4 EXPERIMENTAL RESULTS

To analyze the localization error of the proposed

method, two different navigation paths are used.

Along each navigation path, we drive an outdoor

UGV and acquire a sequence of 3D range data. At the

same time, we record the ground truth positions of the

path in every revolution of the 360 degree range sen-

sor. The sensor is rotated in 10Hz revolution speed.

The UGV used in this experiment is shown in Figure

5(a). A DSM with two navigation regions is shown

in Figure 5(b). Table 1 shows some properties of two

navigation paths.

(a) (b)

Figure 5: (a) An UGV with 360 degree range sensor (b)

DSM.

Table 1: Properties of navigation paths.

Path Path-1 Path-2

# of frames 650 970

DSM size(pixel) 969×2713 1677×1441

DSM accuracy (m) 0.5 0.5

We compare our localization method with an ICP

algorithm. In this ICP algorithm, source points are

sampled by UPS and the k-d tree search algorithm is

also combined when searching the closest points. In

each navigation path, average registration error and

processing time are recorded. For fair comparison,

the number of sampled points of two methods are set

as same as possible.

Table 2 shows the results of two paths. In both

paths, localization time of our method is less than

0.1sec, which means more than 10Hz speed. Local-

ization speed is about three times faster than the ICP

method. Localization error is also smaller than the

ICP.

Table 2: Average localization error and time.

Path Method Error (m) Time (msec)

Path-1 ICP+UPS 6.5 273.9

Proposed 4.63 92.4

Path-2 ICP+UPS 7.27 365.3

Proposed 4.46 97.3

Figure 6 plots the vehicle positions of two experi-

ments with respect to the ground truth. In each ﬁgure,

our method is compared with the ICP method. Plots

of our method are closer to the ground truth. Figure

7 shows localization error in each frame of the test

sequences. Compare to the ICP method, our method

VISAPP 2011 - International Conference on Computer Vision Theory and Applications

612

3930900

3931000

3931100

3931200

3931300

3931400

449300 449400 449500 449600 449700 449800

Y (m)

X (m)

Groundtruth

ICP_UPS

Proposed

(a)

3931000

3931100

3931200

3931300

3931400

3931500

449150 449200 449250 449300 449350 449400 449450 449500 449550 449600

Y (m)

X (m)

Groundtruth

ICP_UPS

Proposed

(b)

Figure 6: Localization path comparison (a)Path-1 (b)Path-

0 100 200 300 400 500 600 700

Position error (m)

Frames

ICP+UPS

Proposed

(a)

0 100 200 300 400 500 600 700 800 900 1000

Position error (m)

Frames

ICP+UPS

Proposed

(b)

Figure 7: Localization error in the test sequences (a)Path-1

(b)Path-2.

200

400

600

800

1000

0 100 200 300 400 500 600 700

Registration time (msec)

Frames

ICP+UPS

Proposed

(a)

200

400

600

800

1000

0 100 200 300 400 500 600 700 800 900 1000

Registration time (msec)

Frames

ICP+UPS

Proposed

(b)

Figure 8: Localization time in the test sequences (a)path-1

(b)path-2.

yields smaller localization error through all frames.

Figure 8 compares localization time of two methods.

There shows high variance in the ICP method, which

means unstable localization. On the contrary, local-

ization of our method is very fast and stable.

5 CONCLUSIONS

Real time localization of an unmanned ground vehi-

cle is introduced. A pairwise registration of 360 de-

gree range data is implemented through a projection-

based iterative alignment. Without searching match-

ing points in 3-D space, the projection-based method

can ﬁnd correspondences in very fast time. To reduce

the number of matching pairs as small as possible,

a uniform arc length sampling method is also pro-

posed. Using the proposed sampling method, match-

ing pairs are uniformly distributed in terms of ground

distance between samples. Experiments are done us-

ing sequences of 3D range data obtained from two

navigation paths. In terms of speed and accuracy, the

proposed method yield better performancethan a con-

ventional method.

ACKNOWLEDGEMENTS

The Authors gratefully acknowledge the supports

in part from UTRC(Unmanned technology Research

Center) at KAIST, originally funded by DAPA, ADD

and MKE under the Core Technology Development

for Breakthrough of Robot Vision Research support

programsupervised by the NIPA (NIPA-2010-C7000-

1001-0006).

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