Robot localization under perceptual aliasing conditions based on laserreflectivity using particle filter
DongXiang Zhang∗ , Ryo Kurazume∗ , Yumi Iwashita∗ , Tsutomu Hasegawa∗
Abstract— Global localization, which determines an accurate
global position without prior knowledge, is a fundamental
requirement for a mobile robot. Map-based global localization
gives a precise position by comparing a provided geometric map
and current sensory data. Although 3D range data is preferable
for 6D global localization in terms of accuracy and reliability,
comparison with large 3D data is quite time-consuming. On
the other hand, appearance-based global localization, which
determines the global position by comparing a captured image with recorded ones, is simple and suitable for real-timeprocessing. However, this technique does not work in the dark
or in an environment in which the lighting conditions change
remarkably. We have proposed a two-step strategy so far, which
combines map-based global localization and appearance-based
global localization. Instead of camera images, which are used
for appearance-based global localization, we use reflectanceimages, which are captured by a laser range finder as a byproduct of range sensing. However, since this method relies on
the similarity between newly-captured and stored reflectanceimages, the performance is deteriorated in a scene with highperceptual aliasing containing few or similar features such asa corridor and a mine. To deal with this problem, in thispaper, we propose a new global localization technique which
combines the proposed two-step strategy and the particle filter.
The effectiveness of the proposed technique is demonstrated
through experiments in real environments.

I. INTRODUCTION
In numerous practical applications, the external environment around a robot is unpredictable, unstructured, anduncontrolled. For example, in an area that has been struck
by a strong earthquake or by a mine disaster, previously
navigable areas may be blocked by heaps of rubble orcollapsed walls, and the geometric structure will differ fromthe original structure. In order for a robot to efficiently
accomplish search and rescue tasks in this type of unknown
and unpredictable environment, accurate map creation andglobal localization are fundamental requirements.
A number of global localization techniques have been proposed [11]. Appearance-based global localization is simple
and suitable for real-time processing. Many camera images
are recorded under natural light or indoor illumination in
the environment, and global localization is performed byfinding the best match using a newly-captured image andstored images[13],[14]. However, appearance-based global
positioning encounters a critical problem in dark environRyo Kurazume, Yumi Iwashita and Tsutomu Hasegawa are with Facultiesof the Graduate School of Information Science and Electrical Engineering,
Kyushu University, Japan
DongXiang Zhang is with Phd candidate of the Graduate School ofInformation Science and Electrical Engineering, Kyushu University, Japan,zhang@irvs.is.kyushu-u.ac.jp

ments or in environments in which the lighting conditionchanges dramatically.
On the contrary, map-based global localization, which
determines the best position at which the observed geometric features from sensory data match those in the provided geometric map, is a standard and reliable techniquesince this method is applicable for various sensors andenvironments[1],[8]. From the point of view of accuracy,comparison of 3D range data captured by a range sensor
and a pre-constructed 3D map is preferable because this will
enable precise 6D(position and attitude) global localization.
However, the comparison with large 3D data is quite timeconsuming.
We have proposed a two-step strategy that combines
map-based global localization and appearance-based global
localization and verified its efficiency in outdoor environmentin [21]. Instead of camera images, which are used for theappearance-based global localization, we used reflectanceimages, which are captured by a laser range finder as a
byproduct of range sensing. As a result of the characteristicsof the reflectance image, which is not subject to significant variation of the external illumination conditions, theproposed technique is useful even in the dark or in anenvironment under severe lighting conditions. Furthermore,fast and precise localization can be performed by comparing
a few 3D range images, which are selected based on thesimilarity of the reflectance images. However, since thismethod relies on the similarity between newly-captured andstored reflectance images, the performance is deteriorated
under perceptual aliasing conditions such as in a corridor orin a mine.
Particle filter localization is an effective method for robotic
application by Bayesian inference. The robustness for robot
localization and mapping in a scene with few or similarfeatures such as a corridor[16],[19], a mine[7] and so on
has been demonstrated. Therefore, we propose a new global
localization technique which combines the proposed twostep strategy and the particle filter. We will demonstrate that
our method can achieve robust and precise robot localizationin environments with strong perceptual aliasing even under
severe variation of external illumination conditions.
The remainder of the present study is organized as follows.
After a brief introduction of related research in Section II,
Section III introduces the cooperative positioning system on
which the proposed method is based. The proposed twostep strategy combined with particle filter is described indetail in Sections IV, and experimental results are presented
in Section VI.

II. R ELATED RESEARCH
In the robotics community, appearance-based localization
using camera images which is very close to the sensing
way of human being has achieved great success. The BoF
technique is successfully applied to the appearance-based
SLAM problem. It first extracts local features of an image
and then constructs a histogram using the extracted local
features as a representation of the global features. In [2],the loop closure detection, which is a problem to find alocation where the robot previously visited, was solved by
the BoF technique in a probabilistic manner. However, thesemethods assume that the illumination condition does notchange so severely. Therefore, the travel distance of the robot
must be short enough so that the lighting condition will notchange drastically. In another study, the experimental results
were not evaluated explicitly under significant changes inillumination [3]. These methods will fail in the dark or inenvironments in which the lighting is dramatically changed.
When range is measured by a laser range finder, thereflectivity, which indicates the strength of the reflected laser,
can be obtained as a byproduct of range data. Note that
all of the pixels in the range image have correspondingreflectance values. In other words, the range image and thereflectance image are precisely and fundamentally aligned.
In addition, since the reflectance image is not subject to any
extreme variations in the external illumination conditions,stable reflectance images can be obtained even at night.
Kara et al.[4] utilizes reflection intensity from a 1D laser
range finder for localization in 2D space. On the other hand,
our technique utilizes 2D reflectance images obtained by
a 3D laser range finder for 3D localization. The proposed
technique uses a panoramic reflectance image instead of aregular camera image. By applying the BoF technique fora reflectance image that corresponds to 3D range data, theglobal localization using 3D range data and 2D images isachieved efficiently. Levinson et al.[8] utilize 2D infrared
reflectivity map to achieve a real-time autonomous vehicle
navigation. Firstly they created a spatial grid map with
infrared remittance values in a probabilistic manner using
SLAM technology and determined the vehicle position online with the 2D infrared reflectivity map and a GPS/IMU
system.
Particle filter(Monte carlo localization) is an useful probabilistic inference algorithm[12]. In particle filter algorithm,
the probabilities of Bayesian inference are represented as
many particles. Particle filter has been applied in manysuccessful robot system. Wolf et al.[16] integrates Monte
carlo localization(MCL) with an image retrieval system.
Andreasson et al.[17] integrates Monte carlo localization
with an panoramic imaging system. Zingaretti et al.[19]compares two different MCL systems, one using SIFT-based
vision system and the other using sonar sensing system, thenit combines these two system to get better localization underperceptual aliasing conditions.
The algorithm framework of the proposed technique issimilar to that described in [16] and [17] which weightthe particles according to the results of the image retrieval
process. However, since these techniques[16][17] depend on
conventional camera images, it apparently would fail under
severe variation of illumination.
III. D EVELOPMENT OF A 3D GLOBAL MAP BY THE
COOPERATIVE POSITIONING SYSTEM

A. Laser-based environmental modeling by multiple mobile
robotsFor map-based global localization, an environmental map
must be created and provided beforehand. For precise 3D
mapping of the environment around a robot, we have proposed an efficient and precise system called CPS-SLAM [5],which can construct a rather accurate large-scale 3D map bymeans of a laser range finder and multiple robots based ontechnology used for geographical surveying. This technique
is used as the basis of the urban search and rescue (USAR)
robot [6].

Fig. 1.

Three-dimensional modeling robots, CPS-VI

TABLE IL ASER RANGE FINDER (SICK LMS151)
Measuring range
Field of view
Precision
Angular resolution

50[m]270[◦]±30[mm]0.25[◦]

Figure 1 shows the sixth CPS-SLAM model, called CPSVI. This system consists of one parent robot and two childrobots. The parent robot is equipped with a highly precise
laser range finder (GPT-9000A, TOPCON LTD), a 2D laser
range finder (SICK LMS151), and a three-axis attitude
sensor. The two child robots are equipped with corner cubes.
The GPT-9000A and corner cubes are used cooperatively for
self-positioning, as shown in Fig. 2. The LMS151 (Table I)
placed on a rotating table acquires two-dimensional slit-likerange data, which are vertical to the ground. This sensor
can capture reflectance data at the same time. Therefore, byrotating the table around the vertical axis for 360 ◦ while
scanning with a 2D laser range finder, 3D range data anda 2D reflectance image are acquired. The number of pixels
on a reflectance image is exactly the same as the number of
3D points in the range data, i.e., there exists a one-to-one
mapping relationship between 2D pixels of the reflectanceimage and 3D points of the local 3D map.
Fig. 4.

Reflectance image and camera image in corridor

Fig. 5.

Two sideviews of local 3D map

used for appearance-based global localization in the proposed
two-step strategy.
D. Image retrieval using the BoF techniqueFig. 2.

Fig. 3.

Cooperative positioning system (CPS)

Construction of a large-scale 3D map by the CPS

When the global 3D map of the target field is constructed,a Kd-tree structure storing BoF representations of reflectanceimages is also constructed at the same time. Reflectanceimages are represented as histograms of occurrence of thevisual words in an image. SIFT and SURF[20][18] are wellused in appearance-based robot localization for extracting
features. First, regions in feature space are mapped to visual
words by clustering all SURF or SIFT features extracted
from recorded images into representative words using kmeans clustering, and the words are stored using a Kd-tree
structure. Using these words as the x-axis, we quantize eachfeature in a reflectance image to its approximate nearest wordby searching the Kd-tree, and all of the recorded reflectanceimages are represented as statistics of words (histograms).
Finally, the histograms of all recorded images are stored
using a Kd-tree structure. A newly-captured image is also
represented as a histogram, and M images that best matchthe newly-captured image are retrieved by quantizing thehistogram to its nearest M histograms. These M candidates
are used as the observation model of particle filter.

B. Three-dimensional global map
The process of mapping the entire field is displayed inFig. 3. In each location, the parent robot collects a local
3D map and its measurement position based on the relative
observation between the parent and child robots. Eventually,
all of the local 3D maps are aligned into a global 3D map
using the measurement position information. Additional details about CPS-based simultaneous localization and mapping(CPS-SLAM) can be found in [5].
C. Reflectance images
As mentioned above, reflectance images are captured asa byproduct of range sensing. An examples of a reflectanceimage and its corresponding camera image taken in the night
are shown in Fig. 4, Two sideviews of the corresponding 3D
data acquired by CPS-VI are shown in Fig. 5. Note that eachreflectance image contains the position information for thelocation at which the image was captured. These images are

IV. T WO - STEP STRATEGYThis section presents the two-step strategy for precise
localization using a 3D map. First, we need to create a
global map as a training dataset. As explained in Section
III, the CPS robots move in the environment and construct a
3D global map. At the same time, the parent robot collects
reflectance images at each measurement position. Then, all ofreflectance images are represented using the BoF technique,and a training dataset is created. Finally, the dataset of allof the BoF representations is stored in a Kd-tree, which isefficient for information retrieval.
For global localization, a new robot that is equipped witha 3D range sensor, such as CPS-VI, collects local 3D data
and 2D reflectance images (test data). In the first step, we
retrieve M initial location candidates by comparing stored
reflectance images (training dataset) and captured reflectanceimages (test data) using the BoF technique and a Kd-tree.

Next, we apply particle filter for extracting a correct initial
location candidate from these M candidates. By using theparticle filter, particles converge to few locations, and false
initial locations are excluded. So only a few initial candidates
are left for the next precise localization. We then apply a 3D
geometric constraint in order to extract true feature pairs andrun automatic ICP in the second step. As a result of the firststep, fast and precise localization can be performed in thesecond step by comparing only one 3D range images, which
is selected based on the similarity of the reflectance images
and probabilistic inference in the first step.
We hereinafter denote Train i.re f and Train i.pts as theith reflectance image and the local 3D map, respectively, inthe training dataset, and Test j.re f and Test j.pts as the jthreflectance image and the local 3D map, respectively, in thetest dataset. In the following, the proposed two-step strategy
is described in detail.
A. First step: image retrieval and Particle filtera) Image retrieval by BoF using 2D reflectance images:
All Test j.re f are converted into BoF representations, andthe M best matches are searched in the Kd-tree that wasconstructed from the training dataset. The M Train i.re f andTrain i.pts are then selected as M candidates for the positionof the robot in the 3D global map.
b) Particle filter: When the reflectance images arecollected, the positions are also recorded by CPS, so a
topological graph in which each node is represented by areflectance image is created. By the results of image retrieval,
M candidates location in the topological graph are used forthe observed data of particle filter. The particles will converge
to one of M candidates. The details are described at section
V.
B. Second step: precise measurement by automatic ICP
using 3D data
With the position of particle after convergence, automatic
ICP [9][10] which consists of two processes is applied.
However, before applying automatic ICP, 3D geometricconstraints are used to remove outliers. In some cases, thenumber of false outliers is large than true inliers, therefore
we have proposed a two-step voting algorithm based on 3D
geometric constraints to remove outliers efficiently in [21].
The process of automatic ICP is as follows:
1) Rough alignment with RANSACa) Find the corresponding features between
Test j.re f and Train i.re f .
b) Get the 3D coordinates of corresponding featuresusing Test j.pts and Train i.pts, which correspond to Test j.re f and Train i.re f , respectively.
c) Remove outliers by two-step voting algorithm.
d) 3D transformation between Test j.pts andTrain i.pts is estimated by RANSAC.
e) Align Test j.pts to Train i.pts.
2) Precise alignment with ICPa) Run ICP[9] using Test j.pts and Train i.pts,
which are already roughly aligned.

V. SIR PARTICLE FILTER
Sequential Importance Resampling(SIR) particle filter isa kind of Bayesian filtering techniques which estimate thebelief recursively. The key idea of particle filter is representing the probabilistic belief p(Xt |o, a) of robot’s location by
a set of weighted particles. The probability of current state
Xt given a history of action and observation (abbreviated asa and o, respectively) is approximated as:
(i)

(i)

St = {xt , πt }, i = 1...N(i)

Each particle xt represents a hypothesized state with a non(i)negative numerical weight πt . The alternating three main
steps of SIR particle filter are as follows:
1) Generate a new set of particles St+1 by resamplingfrom old particle set St according to particle weights(i)πt , i = 1...N
2) Predict the next state of new particle set based on the
motion model with additional Gaussian noise.
3) Calculate the new weights of new particles, this is doneby integrating the observation model into the weightof each particle.
The details how particle filter is applied in our robot system
is described as follows:
A. Motion model
When a robot is visiting the target environments, all(i)(i)
particle variables xt = (x, y, ϕ )t where (x, y) is the robot
position in 2D grid map and ϕ is the orientation, are updated
according to the movement between adjacent measurement
positions which are obtained by odometry or other local
localization techniques such as CPS. Meanwhile the particle
variables are updated with small random values drawn froma normal distribution, using a standard deviation of var yaw
for the rotation and a standard deviation of var x and var yfor the translation.
B. Observation modelIn each location, with the current scanned reflectance( j)image, M candidate positions ot from training datasetof reflectance images are searched out by Kd-tree. In M
candidates only the one whose collecting location is closest
to the current particle is used for calculating its weight. From
the automatic ICP, the rotation angle ϕ can be calculated
correctly, it doesn’t need to be used for calculating weights.
The weighting function is defined as:
fw (d) = exp(−

d2
)
δ2

(1)
(i)

d is the minimum Euclidean distance between xt( j)ot , j = 1...M,
M

(i)

( j)

d = min ||xt − ot ||
j=1

and
(2)

C. Location estimation
The particles will converge to a location at each loop
in the process of SIR. The convergent particles’ locationis the roughly estimated robot’s position. With the current
convergent location, the reflectance image whose location isclosest to it is selected. This means that two correspondingreflectance images are found: one is the current reflectanceimage, and another is the searched reflectance image fromtraining dataset. With this two reflectance images and theircorresponding 3D points data, the robot’s location can be
precisely estimated by automatic ICP.
VI. E XPERIMENT AND R ESULTS
The experiment is conducted in an environment withstrong perceptual aliasing in order to verify the performanceof the proposed two-step strategy. The 2D map for the experiment is shown in Fig. 6 which is created in a corridor. There
are many similar scenes in this path, two pairs of similar
scenes are also shown in Fig. 6, examples of reflectanceimage and corresponding 3D points data have been shown in
Fig. 4 and Fig. 5. In this experiment, the size of reflectanceimages is 590 × 2264 pixels. The CPS-VI robots move andstop at 72 different locations in the experimental area in Fig.
6, i.e. 72 data (Train i.re f and Train i.pts) are stored as thetraining dataset. On the other hand, test data (Test j.re f andTest j.pts) are collected at 36 locations in the same map.
Both the training path and test path are shown in Fig. 7.

In Eq.(1), δ was set to be 2T where T is the average
moving distance between the adjacent collecting positionsof training dataset. Here T is set to be 3.6[m]. With thisvalue of δ , the number of candidates M (see Eq.(2)) is set to
be 6. The particles converge to the correct location after the
5th scanning position (shown as ”04” in Fig.7) in test path.
Table II shows the correctness of the initial localization
within the 6 candidates estimated by BoF, i.e. in which rank
the correct location is chosen within six candidates retrieved
from the training dataset by the Kd-tree for each Test j.re f .
The experimental results show that 31 locations in total arecorrectly estimated within 6 candidates for every Test j.re f .
Especially in 19 positions, the positions of the robot are
correctly estimated as the first candidate. In two positions,the second candidate and the fourth candidate are the actual
locations, respectively. There are 36−31 = 5 positions where
the BoF can’t initially estimate robot’s location correctly.
So the final accuracy is 31/36 ≈ 86.1%. Note that althoughproper initial locations are not obtained at 5 positions fromthe BoF, the correct position is estimated by the automatic
ICP after 5th location thanks to the particle filter.
Particle filter excludes false estimation and finds true
robot’s location. The efficiency of particle filter is verified
by automatic ICP. As mentioned in [21], four non-coplanar
true pairs of matching 3D points are enough for calculationof a rigid transformation. By setting the parameters of voting
algorithm (see [21] for more details), all of right candidate
correspondences can be correctly aligned by automatic ICP.
Since after the location ”04” (i.e. the 5t h location in testpath), all false candidates are excluded by particle filter.
Therefore, correct corresponding Train i.pts and Test j.pts
are found, and robot’s location are all correctly estimated inremaining positions. Figure 8 shows the comparison between
the localization of particle filter and final precise localization
by Automatic ICP. Among the red points, before location”04”, particles don’t converge to correct locations. These
locations are excluded by Auto-ICP and corresponding red
points are not shown in Fig.8. Figure 9 shows an example ofalignment between train 10.pts test 10.pts and 2D featuresof reflectance images. Their locations are shown in Fig.7.
TABLE IIR ESULTS FROM SEARCHING IN B O F

Fig. 6.

Experimental areas

No.
Correct
localization

1st

2nd

3rd

4th

5th

6th

total

accuracy

19

1

5

1

2

3

31

86.1%

VII. C ONCLUSION

Fig. 7. Experimental paths, red line is the training path. blue line is thetest path.

We proposed and demonstrated a two-step strategy combined with particle filter for global localization of a mobile robot in environments with strong perceptual aliasing.
Besides the combination of appearance-based global localization and map-based global localization which improve
the performance of correct position estimation, particle filter

R EFERENCES

Fig. 8. Comparison between the particle filter location and alignment of
automatic ICP.

Fig. 9. From right to left, then top to bottom: Original 3D points; Roughlyaligned 3D points by voting algorithm and RANSAC; Precisely aligned 3D
points by ICP; Matched features in reflectance images

is used for further excluding false initial localization inappearance-based localization. The reflectance image, which
is obtained as a byproduct of range sensing and is independent of the variation of the illumination condition, isused for appearance-based global localization in the firststep. Precise map-based global localization by ICP is applied
using 3D local maps, which are selected by the first step.
The effectiveness of the proposed technique is demonstrated
through experiments in an indoor image sequence with strong
perceptual aliasing.
VIII. ACKNOWLEDGMENTS
The present study was supported in part by a Grant-in-Aid
for Scientific Research (B) (23360115).

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