Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review
Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algor...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/14/10/2019 |
| _version_ | 1797496225594867712 |
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| author | Mengshen Yang Xu Sun Fuhua Jia Adam Rushworth Xin Dong Sheng Zhang Zaojun Fang Guilin Yang Bingjian Liu |
| author_facet | Mengshen Yang Xu Sun Fuhua Jia Adam Rushworth Xin Dong Sheng Zhang Zaojun Fang Guilin Yang Bingjian Liu |
| author_sort | Mengshen Yang |
| collection | DOAJ |
| description | Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed. |
| first_indexed | 2024-03-10T03:01:34Z |
| format | Article |
| id | doaj.art-36d4bb2c0571462e97beb81eb43ddca2 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-10T03:01:34Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-36d4bb2c0571462e97beb81eb43ddca22023-11-23T12:46:10ZengMDPI AGPolymers2073-43602022-05-011410201910.3390/polym14102019Sensors and Sensor Fusion Methodologies for Indoor Odometry: A ReviewMengshen Yang0Xu Sun1Fuhua Jia2Adam Rushworth3Xin Dong4Sheng Zhang5Zaojun Fang6Guilin Yang7Bingjian Liu8Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, Nottingham NG7 2RD, UKNingbo Research Institute, Zhejiang University, Ningbo 315100, ChinaNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, ChinaAlthough Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.https://www.mdpi.com/2073-4360/14/10/2019self-contained localizationodometrySLAMpolymeric sensorstate estimationsensor fusion |
| spellingShingle | Mengshen Yang Xu Sun Fuhua Jia Adam Rushworth Xin Dong Sheng Zhang Zaojun Fang Guilin Yang Bingjian Liu Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review Polymers self-contained localization odometry SLAM polymeric sensor state estimation sensor fusion |
| title | Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review |
| title_full | Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review |
| title_fullStr | Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review |
| title_full_unstemmed | Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review |
| title_short | Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review |
| title_sort | sensors and sensor fusion methodologies for indoor odometry a review |
| topic | self-contained localization odometry SLAM polymeric sensor state estimation sensor fusion |
| url | https://www.mdpi.com/2073-4360/14/10/2019 |
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