=Paper=
{{Paper
|id=Vol-2487/sdpaper6
|storemode=property
|title=Semantic Information-based Reliable Autonomous Navigation in Wide Space
|pdfUrl=https://ceur-ws.org/Vol-2487/sdpaper6.pdf
|volume=Vol-2487
|authors=Taeyoung Uhm,Ji-Hyun Park,Gi-Deok Bae,Jung-Woo Lee,Young-Ho Choi,SangYong Han
}}
==Semantic Information-based Reliable Autonomous Navigation in Wide Space==
https://ceur-ws.org/Vol-2487/sdpaper6.pdf
The 1st International Workshop on the Semantic Descriptor, Semantic Modelingand Mapping for Humanlike
Perceptionand Navigation of Mobile Robots toward Large Scale Long-Term Autonomy (SDMM19)
Semantic Information-based Reliable Autonomous
Navigation in Wide Space
Taeyoung Uhm, Ji-Hyun Park, Gi-Deok Bae, Jung-Woo Lee, Young-Ho Choi,
Korea Institute of Robot and Technology Convergence Pohang, Republic of Korea
{uty,jipark, bgd9047, ricow, rockboy}@kiro.re.kr
Sang-Yong Han
Kookmin University, Seoul, South Korea
syhan@kookmin.ac.kr
Abstract
Recently, much attention has been paid to intelligent robots. Espe-
cially, autonomous navigation of robots is the most important tech-
nology and is being developed by many researchers. The autonomous
navigation technology is based on the SLAM to find the position from
the sensor data that is mounted robots. However, most methods find
locations based on high-performance sensors in the predefined envi-
ronments. This is difficult to apply to complex environments such as
wide area due to the difference in locomotion and sensor performance.
Therefore, efforts should be made to improve the application of limited
navigation technology. In this paper, we propose a method to drive in
a wide space for robots with various locomotion and sensor by semantic
information based autonomous navigation method. By using semantic
information, the robot recognizes the surroundings using available sen-
sor data and performs autonomous travel. For this purpose, a semantic
map for a unit space (e.g. a room, a hallway, road etc.) is generated
and traveled by receiving information suitable for a robot locomotion
and sensor configuration from the local server. The proposed method
utilizes the semantic map to drive in the same way as a person in a
large space, and can use intelligent robot driving using the property in-
formation of the object. Therefore, it is expected that industrialization
of robot autonomous navigation will be promoted.
1 Introduction
Research on the autonomous mobile robot has been done steadily. Recently, robots employ various locomotion
and sensors [Khazanov14]. These researches assign robots by task to perform a specific task [Amigoni05], or
use semantic information to service in a limited space indoors [Lim10]. In addition, there are studies that
use task management, environmental awareness, trajectory planning, decision making and terrain classification
using semantic maps and ontology for robot mapping [Liu12], [Li12]. However, these robots mainly carry out
autonomous study on predefined areas in a way limited to locomotion and sensor system. In this paper, we
Copyright c 2019 by the paper’s authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY
4.0).
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The 1st International Workshop on the Semantic Descriptor, Semantic Modelingand Mapping for Humanlike
Perceptionand Navigation of Mobile Robots toward Large Scale Long-Term Autonomy (SDMM19)
propose a novel autonomous navigation method that can travel a wide area through a map suitable for semantic
DB-based robot motion and sensor systems developed for intelligent robots. The proposed method simulates
Figure 1: Semantic DB based Reliable Autonomous Navigation
the navigation method used by human beings and drives similarly. Robots travel and recognize objects around
them as humans do. To do this, the robot registers and recognizes the objects necessary for navigation, as shown
in 1. After that, if the recognized objects are obstacles, they will be avoided or waited after depending on the
properties of the motion defined by the semantic information. If the recognized object is an object that affects
driving (e.g. a rough road), the driving speed is adjusted according to the drivability defined in the object’s
properties.
Meanwhile, a wide-area spatial map based on 3D LiDAR sensor and vision sensor is constructed to drive
wide-area space suitable for various robot motions and sensors. Robots can use this map to recognize the exact
location from the multi-sensor data or even a single sensor. Therefore, the proposed driving method is useful in
the wide area where many people walk because they drive according to semantic information. This is expected
to bring dramatic developments to the autonomous driving of robots.
2 Navigation Method using Semantic Information
The semantic information used for autonomous driving of robots is based on objects. The robot recognizes the
object while driving and uses semantic information of the object to secure driving ability similar to humans.
This can be divided into two abilities: First, the motion property is used as semantic information of the object.
Using this, an object, such as a person or a chair occupied by a person, is recognized as a movable object and
waits for 5 seconds when the robot meets a moving obstacle while driving. After that, it is avoided in the same
manner as a fixed obstacle, as shown in 2.
Figure 2: Semantic DB based Reliable Autonomous Navigation
Second, the semantic information used is the drivability property of the object corresponding to the driving
road. If you are driving on a road where there is a rough property, change the speed to match the degree of
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The 1st International Workshop on the Semantic Descriptor, Semantic Modelingand Mapping for Humanlike
Perceptionand Navigation of Mobile Robots toward Large Scale Long-Term Autonomy (SDMM19)
drive. The degree of roughness is divided into 5 levels and it is possible to secure the driving stability of the
robot. 3 shows the difference in running speed on gravel and asphalt roads.
Figure 3: Semantic DB based Reliable Autonomous Navigation
3 Semantic DB based Map Building
The semantic DB is created by modeling object information necessary for driving from the viewpoint of the
robot. Based on this, a map that is suitable for the locomotion and sensor system of the robot is build. This is
a key factor for driving the robot in a wide space. 4 and 5show the entire flow chart for creating the map and
the robot platform used for building the map. The platform was used to build a map based on the point cloud
of the 3D LiDAR sensor and the ORB features of the vision sensor [Mur17]. 6 shows wide-area spatial maps for
each sensor. It is possible to generate a semantic map including semantic information in a map suitable for the
robot sensor.
Figure 4: Semantic DB based Reliable Autonomous Navigation
Based on this semantic map, the robot performs autonomous Navigation. The robot sets up the driving
strategy according to the properties of the recognized object (e.g. movable, rough etc.). Using this method, the
autonomous mobile robot can flexibly move in the wide area.
4 Experimental Results
Using the proposed method, autonomous driving was carried out in a wide area of about 6ooo m2, as shown in
7.
This was tested during the exhibition at the convention center. First, the robot received information about
the semantic map and mission (patrol) from the local server and started driving. Next, the robot traveled to pass
between the people standing in line for entry and waited a while when it could no longer drive. The robot then
continued to drive through the gaps caused by people’s movement. As shown in the results, it was possible to
drive smoothly in an environment with dense crowds and to travel between crowds using semantic information.
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The 1st International Workshop on the Semantic Descriptor, Semantic Modelingand Mapping for Humanlike
Perceptionand Navigation of Mobile Robots toward Large Scale Long-Term Autonomy (SDMM19)
Figure 5: Semantic DB based Reliable Autonomous Navigation
Figure 6: Semantic DB based Reliable Autonomous Navigation
Figure 7: Autonomous Navigation Results in Wide-Area
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The 1st International Workshop on the Semantic Descriptor, Semantic Modelingand Mapping for Humanlike
Perceptionand Navigation of Mobile Robots toward Large Scale Long-Term Autonomy (SDMM19)
5 Conclusion and Future Work
In this paper, we propose autonomous navigation method in wide area by generating semantic map suitable
for locomotion and sensor system of various robots based on the driving method performed by humans. For
this purpose, driving using multi-sensor based semantic navigation map and defined semantic information was
performed. In the future, we will apply semantic information-based autonomous driving methods that can be
used for more types of locomotion and sensors.
Acknowledgment
This work was supported by the Korean Evaluation Institute of Industrial Technology and conducted by the
Ministry of Industry and Commerce in 2017 (Industrial Core Technology Development Project, Project Number
10080489) and 2018 (Industrial Core Technology Development Project, Project Number 20000683).
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