2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Daejeon Convention Center
October 9-14, 2016, Daejeon, Korea

Multi-modal Panoramic 3D Outdoor Datasets for Place Categorization
Hojung Jung1 , Yuki Oto1 , Oscar M. Mozos2 , Yumi Iwashita3 and Ryo Kurazume4

Abstract— We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization withsix categories: forest, coast, residential area, urban area andindoor/outdoor parking lot. The first dataset consists of 650
static panoramic scans of dense ( 9,000,000 points) 3D color andreflectance point clouds obtained using a FARO laser scannerwith synchronized color images. The second dataset consistsof 34,200 real-time panoramic scans of sparse ( 70,000 points)
3D reflectance point clouds obtained using a Velodyne laser
scanner while driving a car. The datasets were obtained in thecity of Fukuoka, Japan and are publicly available in [1], [2].
In addition, we compare several approaches for semantic place
categorization with best results of 96.42% (dense) and 89.67%(sparse).

I. INTRODUCTION
Understanding the surrounding environment is an important capability for autonomous robots and vehicles thatallows them to identify their current type of location. This
information greatly improves communication between robots
and humans [3], [4] and it allows autonomous robots to makedecisions with context-based understanding when completinghigh-level tasks [5]. Moreover, if a robot has the ability tocategorize places according to their type, then it will be able
to properly execute a task even in unfamiliar surroundings.
In addition, autonomous vehicles can make decisions basedon the environmental conditions.
This paper focuses on semantic categorization of placesin outdoor scenarios where a mobile robot or vehicle should
determine the type of place where it is located. Examples ofoutdoor places are shown in Figure 1. Place categorization
using 2D images has been achieved in high-level image
understanding. 2D images can be captured by a camera or
be collected by search engines, therefore, 2D image understanding and scene recognition are actively researched andevaluated by using several datasets, such as ImageNET [6],SUN database [7], or Places2 [8]. The ImageNET and
Sun datasets consist of a large collection of images withvariability in visual appearance, which can be searched using
category terms. The Places2 dataset includes images from
1 Hojung Jung and Yuki Oto are with Graduate School of Information
Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395,
Japan {hojung,y oto}@irvs.ait.kyushu-u.ac.jp
2 Oscar Martinez Mozos is with Technical University of Cartagena(UPCT), Spain oscar.mozos@upct.com
3 Yumi Iwashita is with Jet Propulsion Laboratory, 4800, Oak
Grove Drive Pasadena, 91109 CA, USA. This research was performed
when she was at Kyushu University, and this paper does not contain any results from the researches at Jet Propulsion Laboratory.
Yumi.Iwashita@jpl.nasa.gov
4 Ryo Kurazume is with Faculty of Information Science andElectrical Engineering, Kyushu University, Fukuoka 819-0395, Japankurazume@ait.kyushu-u.ac.jp

978-1-5090-3761-2/16/$31.00 ©2016 IEEE

Fig. 1. Top image: Map indicating the location where dense panoramic
images were obtained. Bottom image: Example panoramic images for forest(1), coast (2), indoor parking lot (3), outdoor parking lot (4), residential area(5), and urban area (6).

different scene categories that are used to train convolutional
neural networks (CNN) [9]. New images can be classified
using the provided CNN models. However, those datasets
do not provide 3D information.
To extend the scope of 2D images, 3D place categorization
has been studied. In indoor environments, several researchers
used low cost RGB-D sensors to provide sequences of depth
and color point clouds. For example, the NYU-Depth V2
dataset [10] contains segmented 3D images of indoor environments with per-frame accelerometer data. The 3DSUN
RGB-D dataset [11] is an annotated benchmark scene for
3D scene/place categorization and reconstruction. Finally, theKyushu University Kinect Place Recognition Database [12]contains RGB-D sequences of indoor places divided into six
categories, i.e. corridor, kitchen, lab, office, study room andtoilet. In addition, laser scans have been used to increase therange and resolution of panoramic point clouds in indoor
place datasets, and to add reflectance information like in the
Kyushu University Indoor Semantic Place Dataset [13].
Outdoor 3D datasets for place categorization are lesscommon since they need more expensive sensors and ve-

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hicles. The majority of available 3D datasets are used for
localization and mapping [14], [15] but not for high level
semantic place categorization and therefore they do notinclude place labels. Only the KITTI [16] dataset includes
four categories in the labeled scans: city, residential, roadand campus. However, this dataset is used as benchmark
for other purposes such as optical flow, visual odometry, 3D
object detection or 3D tracking. In comparison to the KITT
dataset our dense MPO datasets include higher resolution
panoramic 3D point clouds. In addition all our panoramic
instances include a place label from our six categories.
In this paper, we present two Multi-modal Panoramic
3D Outdoor (MPO) datasets for outdoor semantic place
categorization in static and dynamic environments for mobile
robots and autonomous vehicles. Our datasets are recordedby multi-modalities sensors, and provided as multi-resolution
point clouds. Each dataset consists of six place categories
including ‘forest’, ‘coast’, ‘indoor parking lot’, ‘outdoor
parking lot’, ‘residential area’ and ‘urban area’.
The dense MPO dataset consists of static panoramic scansof dense 3D color and reflectance point clouds as shown in
Figure 1, while the sparse MPO dataset consists of realtime panoramic scans of sparse 3D reflectance point clouds
recorded while driving a car. The datasets were obtained in
the city of Fukuoka, Japan, and are publicly available in [1],[2]. The main motivation for creating two different sensor
modalities datasets (dense and sparse) for the same outdoor
places is to study different sensor performances, and thus
present the researchers with different options when selecting their sensors. In this paper we use time-of-flight laser
scanners which have the advantage of their robustness undervarious environmental conditions, such as bright sunlight,
darkness, and sudden illumination changes.
In addition, we applied several approaches for place categorization to the MPO datasets using different global descriptors and we obtain best categorization results of 96.42%(dense) and 89.67% (sparse) using local binary patterns [13].
II. MPO DATASETS
We present two Multi-modal Panoramic 3D Outdoor(MPO) Datasets for semantic place categorization in
static and dynamic environments for mobile robots andautonomous vehicles. The first dataset contains dense
panoramic point clouds obtained using a FARO sensor. The
second dataset contains sparse point clouds acquired using a
Velodyne sensor.
A. DENSE MPO DATASET
The dense MPO dataset consists of 650 static panoramicscans of high resolution dense 3D point clouds obtainedusing a FARO Focus3D laser scanner. Each panoramic scanis composed of three synchronized sensor modalities: depth,
reflectance, and RGB color.
The FARO Focus3D sensor has a maximum range of 150
meters and a field of view of 360◦ horizontally and × 300◦
vertically (Figure 2). Our dataset contains 3D panoramic
point clouds of 5140 × 1757 pixels (total of 9,030,980

Fig. 2. Left: Vehicle setup for data acquisition. Right Top: Velodyne HDL32E laser scanner (2), Kodak PIXPRO SP360 camera (3), and GARMIN
GPS 18x LVC (4) for the sparse MPO dataset. Right Bottom: FARO
Focus3D sensor (1) for dense MPO dataset.

pixels) with 0.07◦ horizontal and 0.17◦ vertical angular resolution. This sensor is additionally equipped with a rotatingcamera which is calibrated with the laser sensor.
The FARO sensor was installed on top of a vehicle at aheight of 1.8 meters as shown in Figure 2. Each scan was
obtained in a static way with the vehicle stopped. The measurement time needed to obtained one synchronized depth
and color panoramic scan was 3 minutes. Approximately,
the depth and reflectance images takes 1 minute, and color
images take 2 minutes. For each scan we moved the car 10100 meters, stopped and took the full panoramic scan andcolor images. We repeated this process several times in eacharea. Point clouds and color images were synchronized offline using the SCENE software provided by FARO, which
processes and manages scanned data easily and efficiently
by recognizing primitive objects as well as scan registrationand positioning.
3D panoramic scans were label with the corresponding
category (‘forest’, ‘coast’, ‘indoor parking lot’, ‘outdoor
parking lot’, ‘residential area’ or ‘urban area’.) The full
dataset contains 650 panoramic scans distributed among thesix categories according to Table I. For each category werecorded seven sets of scans corresponding to different physical places inside the same category. Example 3D panoramicscans are shown in Figure 3 in their three sensor modalities:
depth, reflectance, and RGB color. The full dense MPO
dataset is publicly available in [1].
Each panoramic scan in the dense MPO dataset is stored
in a PTX file, which contains a header and one line for eachpoint in the format ”X, Y, Z, intensity, R, G, B.” Full details
on this files can be obtained from the dataset’s website [1].
B. SPARSE MPO DATASETThe sparse MPO dataset contains a total of 34,200 sparse
3D panoramic scans obtained using a Velodyne HDL-32E
laser scanner sensor. This sensor contains a vertical fieldof view of 41.3◦ ranging from 10.67◦ to −30.67◦ anda horizontal field of view of 360◦ . Each panoramic 3D
panoramic point cloud has size of 32 × 2166 with horizontal

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TABLE I

ForestCoast

Indoor parking lot

Outdoor parking lot

Residential area

Urban area

D ISTRIBUTION OF 3D PANORAMIC SCANS BY CATEGORY IN THE
DENSE MPO DATASET
Category
Coast

ForestIndoor parking
Outdoor parking
Residential area
Urban area

Number of scans by location
Set1

Set2

Set3

Set4

Set5

Set6

Set7

14
16
16
15
14
16

14
16
12
17
14
16
16
17
18
16
16
17
16
13
15
17
13
13
17
16
15
15
14
16
16
14
15
16
15
16
17
16
16
15
16
16
Total number of panoramic scans

Total
103
116
105
108
106
112
650

TABLE IID ISTRIBUTION OF 3D PANORAMIC SCANS BY CATEGORY IN THE
S PARSE MPO DATASET
Category
Coast
ForestIndoor parking lot
Outdoor parking lot
Residential area
Urban area
Total scans

Set1
Set6
511
376
440
720
520
343
874
536
674
973
490
566
3509
3514

Number of scans by location
Set2
Set3
Set4
Set5
Set7
Set8
Set9
Set10
254
571
221
314
872
506
386
287
824
980
707
730
439
311
797
531
357
274
873
583
466
592
344
428
579
388
370
477
581
563
460
617
787
667
724
563
717
720
977
662
572
587
487
410
712
565
606
739
3373
3467
3382
3077
3787
3257
3570
3264

Total
4298
6479
4780
5445
7464
5734
34200

angular resolution of 0.17◦ and vertical angle resolution of
1.33◦ . Scans were acquired at 2 Hz of sampling frequency.
In addition the dataset contains GPS information obtainedby a Garmin GPS 18x LVC, which stored in NMEA 0183
format at a frequency of 2 Hz.
Additionally, the dataset provides panoramic color images
captured by Kodak PIXPRO SP360 camera a reference of
scanning environment. Each color image covers 360◦ andhas a resolution of 16.36 Megapixels. Images were acquired
6∼7 Hz.
The sensors where located on a vehicle as shown in Fig. 2.
The sparse data was acquired while driving a vehicle at30∼50 kph throw different areas of Fukuoka city as shownin Fig. 1. The maximum displacement error of a scan is 1.4m
at the maximum velocity of 50kph with 10Hz of scanningspeed for a single scan.
The dataset is divided into the same six place categories as
the dense MPO dataset, i.e. ‘forest’, ‘coast’, ‘indoor parking
lot’, ‘outdoor parking lot’, ‘residential area’ or ‘urban area’.
For each category we recorded seven trajectories corresponding to different physical places inside the same category.
Table II shows the distribution of the sparse MPO dataset.
The full sparse MPO dataset is available at [2].

Fig. 3.
Dense MPO Dataset: examples of high-resolution range (top),reflectance (middle) and color (bottom) panoramic images for six outdoor
place categories: forest, coast, indoor/outdoor parking lot, residential andurban area. In range images, brighter colors indicate closer distances and inreflectance images, darker colors indicate higher intensity.

A. Feature Descriptors
In this work we compared three feature descriptors that canbe applied to 3D and multimodal sensor cues: local binary
patterns (LBP) [17], spin images [18], and textons [19].
1) Local Binary Pattern: LBP [17] is a visual transformation applied to greyscale images that describes therelationship between the values of each pixel and theirneighbors. Formally, given a pixel i = (xi , yi ) in an image
I, and its P-neighborhood NP (i), the new decimal value for
pixel i in the transformed imaged ILBP is given by:
P−1

ILBP (i) = ∑ LBP(I(i) − I( jk ))2k , ∀ jk ∈ NP (i),III. PLACE CATEGORIZATION IN MPO DATASETSIn this paper we present several approaches for semantic
place categorization and compare their their effectiveness in
our two MPO datasets.

(1)

k=0

where { j0 , j1 , · · · , jP−1 } are the pixels in the P-neighborhood
NP (i) and LBP(.) is a binary operator over the difference zof two pixels’ values defined as:4547

Forestpaper we applied spin image in a scanner-oriented view pointin cylindrical coordinate system rather than in an objectoriented view point using 3D surface of object. A entire 3D
point cloud can be represented as the scanner-oriented image
with the cylindrical coordinate system when the position oflaser scanner is (0,0,0) for each scan data. To achieve thiswe use the tangent plane through oriented perpendicularly ton and the line through parallel to [18].
3) Texton: Texton [19] is a filter-based technique basedon Leung-Malik filter banks and the maximum response
filter bank contains filters at multiple orientations and scales.
In standard texton, the maximum response filter is appliedamong several Gaussian and Laplacian of Gaussian filterswith the same scale, but different orientations are selected.
In our case, we adopted the maximum response filter among
filters with different scales but same orientation in order toevaluate the directions of edges and not the size.

Coast

Indoor parking lot

Outdoor parking lot

B. Classification Method
Residential area

Urban area

Fig. 4. Sparse MPO Dataset: examples of range (top) and reflectance(bottom) panoramic images for six outdoor place categories: forest, coast,
indoor/outdoor parking lot, residential and urban area. In range images,
brighter colors indicate closer distances and in reflectance images, darker
colors indicate higher intensity.

We use support vector machines (SVM) [21], [22] with aradial basis function (RBF) kernel for the final categorizationof places. Multi-class classification is performed by a “oneagainst-one” approach [23]. In our experiments, we use the
LIBSVM library [24]. Following the method reported in [25],the parameters C and γ are selected by a grid search using
cross-validation. The ranges of C and γ are C ∈ [2−1 , · · · , 220 ]and γ ∈ [2−20 , · · · , 20 ] in the grid search.
For trajectories in the sparse MPO dataset we additionally
apply a classic linear-time majority vote technique [26] forconsecutive frames as follows. Given a time t, we define M
previous consecutive frames {Ct ,Ct−1 , · · · ,Ct−M }. Finally amajority vote is obtained from the classification results of
the M as:Ct Final = Majority vote(Ct ,Ct−1 , · · · ,Ct−M ).


LBP(z) =1 if z ≥ 0
0 if z < 0(2)Each new pixel value in the transformed image image ILBP isa decimal values d in the range [0, ..., 255]. In a final step, thetransformed image ILBP is represented by a histogram hLBP
of length l in which each bin hLBP (l) indicates the frequency
of appearance of the decimal value d as:
hLBP (l) = ∑ I (ILBP (i) = d),(3)iwhere I (.) denotes the indicator function which returns 1
if its argument is true, and 0 otherwise. In our case, the
LBP values are restricted to the 8-neighborhood, and thus
the dimension of the final histogram is 256. LBPs have beensuccessfully applied to categorization of indoor and outdoor
categorization in single and multiple sensor modalities [12],[13], [20].
2) Spin image: The spin image [18] is one of popular
technique for surface matching and 3D object recognition.
The conventional spin images encode the global propertiesof any surface in an object-oriented coordinate system. In this(4)

where Ct indicates the classification of frame t using SVM.
IV. EXPERIMENTAL EVALUATION
We applied the previous approaches for place categorization to our dense and sparse MPO datasets respectively.
A. DENSE MPO DATASET
We applied the previous feature descriptors and a SVM
classifier to the dense MPO dataset using different modalities independently. The classification results are shown in
Table III. Best classification results are obtained using LPBs.
These results are in accordance with our previous work [13].
In particular, reflectance data provide the highest correctclassification ratio. Detailed classification results using LBP
and reflectance are shown in Table IV for all six place
categories.
In addition, we present results combining laser and reflectance modalities as in [12] . Combination of modalities isachieved by concatenating the feature vectors of each modality into a single vector, and then applying SVMs [12]. This
multi-modal categorization gets a CCR of 95.67 ± 3.69%.

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TABLE IV
C ONFUSION MATRIX FOR REFLECTANCE LBP IMAGES FOR THE DENSE MPO DATASET (CCR%)

Coast
ForestIndoor parking
Outdoor parking
Residential area
Urban area

Coast
93.45 ± 17.39
8.95 ± 8.27
0.00 ± 0.00
1.91 ± 2.92
1.33 ± 2.68
0.00 ± 0.00

Forest6.54 ± 17.39
91.04 ± 8.27
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00

Indoor parking
0.00 ± 0.00
0.00 ± 0.00
100.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00

Outdoor parking
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
98.08 ± 2.92
0.00 ± 0.00
0.00 ± 0.00

Residential area
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
97.32 ± 4.43
1.87 ± 4.00

Urban area
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
1.33 ± 4.00
98.12 ± 4.00

TABLE VC ONFUSION MATRIX (CCR %) OF LBP IMAGES WITH MAJORITY VOTE FOR THE SPARSE MPO DATASET.

Coast
ForestIndoor parking
Outdoor parking
Residential area
Urban area

Coast
82.99 ± 4.19
8.64 ± 1.12
0.00 ± 0.00
0.96 ± 0.06
0.45 ± 0.01
0.00 ± 0.00

Forest5.20 ± 0.54
90.87 ± 1.20
3.31 ± 0.01
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00

Indoor parking
0.00 ± 0.00
0.00 ± 0.00
93.95 ± 0.65
5.25 ± 0.12
0.00 ± 0.00
0.00 ± 0.00

Outdoor parking
7.15 ± 2.20
0.49 ± 0.01
2.16 ± 0.56
89.16 ± 1.31
2.70 ± 0.01
3.84 ± 0.76

Residential area
1.50 ± 0.05
0.00 ± 0.00
0.59 ± 0.02
2.44 ± 0.26
93.23 ± 0.20
8.76 ± 0.79

Urban area
3.16 ± 0.56
0.00 ± 0.00
0.00 ± 0.00
2.19 ± 0.26
3.63 ± 0.20
87.39 ± 0.20

TABLE IIIC ORRECT CLASSIFICATION RATIO (CCR %) RESULTS FOR THE DENSEMPO DATASET.
Descriptor
Spin image [18]LBP [13]
Texton [19]Range
89.43 ± 0.00
94.35 ± 2.67
89.04 ± 5.58

Reflectance
96.42 ± 2.68
73.59 ± 13.73

Greyscale
93.86 ± 3.85
81.81 ± 10.67

TABLE VI
C ORRECT CLASSIFICATION RATIO (CCR %) FOR THE SPARSE MPODATASET

Spin image [18]LBP [13]

Single frame
79.23 ± 4.51
83.98 ± 4.59

Majority vote88.34 ± 0.12
89.67 ± 0.21
Fig. 5.

Majority vote categorization for different consecutive frames M.

B. SPARSE MPO DATASET
We present categorization results for the sparse MPO
dataset using range data by comparing single observations
VS a majority vote over the last M frames. Table VI shows
the corresponding CCRs. For the majority vote we selected
M = 40 since it provided the best categorization results according to Fig. 5. According to this results the majority voteapproach significantly improves the outdoor categorization
results. Finally we present detailed categorization results forthe six place categories using LBP and majority vote inTable V.

categorization results are thought to be used as benchmark
for researches interested in outdoor semantic place categorization.
ACKNOWLEDGMENT
The present study was supported in part by a Grant-in-Aid
forScientific Research (A) (26249029).

V. CONCLUSIONSWe presented two multi-modal panoramic 3D outdoor(MPO) datasets for semantic place categorization with six
categories: forest, coast, residential area, urban area andindoor/outdoor parking lot. While the dense MPO dataset
contains static high density multi-modal panoramic 3D point
clouds, the sparse dataset contains sparse panoramic point
clouds captured by a moving vehicle. Both MPO datasets are
publicly available on the Internet under [1], [2]. In addition
we have presented preliminary categorization results using
different approaches. The datasets together with preliminary
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