Performance evaluation of ROS-based SLAM algorithms for handheld indoor mapping and tracking systems

Simultaneous Localization and Mapping is an important field of work not only in robotics, but also in mobile platforms. This research work provides insight into how SLAM techniques are deployed in an indoor environment to aid first responders with their duties. Due to the hazardous nature of the env...

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Published inIEEE sensors journal Vol. 23; no. 1; p. 1
Main Authors Nguyen, Quang Huy, Johnson, Princy, Latham, David
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Cartographer
Cartography
Emergency response
Gmapping
Hazardous areas
HectorSLAM
Indoor environments
Indoor mapping
LiDAR
Performance evaluation
Platforms
Robotics
Robots
ROS
Simultaneous localization and mapping
SLAM
Tracking systems
Online AccessGet full text
ISSN1530-437X
1558-1748
DOI10.1109/JSEN.2022.3224224

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Abstract Simultaneous Localization and Mapping is an important field of work not only in robotics, but also in mobile platforms. This research work provides insight into how SLAM techniques are deployed in an indoor environment to aid first responders with their duties. Due to the hazardous nature of the environment and the need for sensitivity due to potential involvement of human subjects, autonomous robots cannot be used. So, the first responders must carry the scanning equipment and perform SLAM at the same time. As a result, unlike standard robot platforms, there will be no reliable odometry source, and SLAM will have to deal with the user's unpredictable movement. In this work, we compare and examine ROS-based SLAM approaches without using any odometry for their application in the above-mentioned circumstances. Gmapping, HectorSLAM, and Cartographer have been chosen as the candidates for this evaluation. We evaluated these approaches in two different environments: a lab office, and a long corridor. The research results show that Cartographer outperforms the other two techniques in our test setup in terms of map quality and trajectory tracking. The Cartographer's mapping error ranged from 0.017m to 0.3548m.
AbstractList Simultaneous Localization and Mapping is an important field of work not only in robotics, but also in mobile platforms. This research work provides insight into how SLAM techniques are deployed in an indoor environment to aid first responders with their duties. Due to the hazardous nature of the environment and the need for sensitivity due to potential involvement of human subjects, autonomous robots cannot be used. So, the first responders must carry the scanning equipment and perform SLAM at the same time. As a result, unlike standard robot platforms, there will be no reliable odometry source, and SLAM will have to deal with the user's unpredictable movement. In this work, we compare and examine ROS-based SLAM approaches without using any odometry for their application in the above-mentioned circumstances. Gmapping, HectorSLAM, and Cartographer have been chosen as the candidates for this evaluation. We evaluated these approaches in two different environments: a lab office, and a long corridor. The research results show that Cartographer outperforms the other two techniques in our test setup in terms of map quality and trajectory tracking. The Cartographer's mapping error ranged from 0.017m to 0.3548m.
Simultaneous localization and mapping (SLAM) is an important field of work not only in robotics, but also in mobile platforms. This research work provides insights into how SLAM techniques are deployed in an indoor environment to aid first responders with their duties. Due to the hazardous nature of the environment and the need for sensitivity due to the potential involvement of human subjects, autonomous robots cannot be used. So, the first responders must carry the scanning equipment and perform SLAM at the same time. As a result, unlike standard robot platforms, there will be no reliable odometry source, and SLAM will have to deal with the user’s unpredictable movement. In this work, we compare and examine robotic operating system (ROS)-based SLAM approaches without using any odometry for their application in the above-mentioned circumstances. Gmapping, HectorSLAM, and Cartographer have been chosen as the candidates for this evaluation. We evaluated these approaches in two different environments: a lab office and a long corridor. The research results show that Cartographer outperforms the other two techniques in our test setup in terms of map quality and trajectory tracking. The Cartographer’s mapping error ranged from 0.017 to 0.3548 m.
Author Johnson, Princy
Nguyen, Quang Huy
Latham, David
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Snippet Simultaneous Localization and Mapping is an important field of work not only in robotics, but also in mobile platforms. This research work provides insight...
Simultaneous localization and mapping (SLAM) is an important field of work not only in robotics, but also in mobile platforms. This research work provides...
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SubjectTerms Algorithms
Cartographer
Cartography
Emergency response
Gmapping
Hazardous areas
HectorSLAM
Indoor environments
Indoor mapping
LiDAR
Performance evaluation
Platforms
Robotics
Robots
ROS
Simultaneous localization and mapping
SLAM
Tracking systems
Title Performance evaluation of ROS-based SLAM algorithms for handheld indoor mapping and tracking systems
URI https://ieeexplore.ieee.org/document/9967952
https://www.proquest.com/docview/2759387333
Volume 23
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