A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review
For the past decade, the main problem that has attracted researchers’ attention in aerial robotics is the position estimation or Simultaneous Localization and Mapping (SLAM) of Unmanned Aerial Vehicles (UAVs) where the GPS signal is poor or denied. This article reviews the strengths and weaknesses o...
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| Language: | English |
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Riga Technical University Press
2022-06-01
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| Series: | Electrical, Control and Communication Engineering |
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| Online Access: | https://doi.org/10.2478/ecce-2022-0007 |
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| author | Rauf Abdul Irshad Muhammad Jehanzeb Wasif Muhammad Mehmood Zubair Kiren Tayybah Siddique Nazam |
| author_facet | Rauf Abdul Irshad Muhammad Jehanzeb Wasif Muhammad Mehmood Zubair Kiren Tayybah Siddique Nazam |
| author_sort | Rauf Abdul |
| collection | DOAJ |
| description | For the past decade, the main problem that has attracted researchers’ attention in aerial robotics is the position estimation or Simultaneous Localization and Mapping (SLAM) of Unmanned Aerial Vehicles (UAVs) where the GPS signal is poor or denied. This article reviews the strengths and weaknesses of existing methods in the field of aerial robotics. There are many different techniques and algorithms that are used to overcome the localization and mapping problem of these UAVs. These techniques and algorithms use different sensors, such as Red Green Blue-Depth (RGB_D), Light Detecting and Ranging (LIDAR), and Ultra-wideband (UWB). The most common technique is used, i.e., probability-based SLAM, which uses two algorithms: Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF). LKF consists of five phases and this algorithm is just used for linear system problems. However, the EKF algorithm is used for non-linear systems. Aerial robots are used to perform many tasks, such as rescue, transportation, search, control, monitoring, and different military operations because of their vast top view. These properties are increasing their demand as compared to human service. In this paper, different techniques for the localization of aerial vehicles are discussed in terms of advantages and disadvantages, practicality and efficiency. This paper enables future researchers to find the suitable SLAM solution based on their problems; either the researcher is dealing with a linear problem or a non-linear problem. |
| format | Article |
| id | doaj-art-96a44c7493244b01834d446058a59a8e |
| institution | Kabale University |
| issn | 2255-9159 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Electrical, Control and Communication Engineering |
| spelling | doaj-art-96a44c7493244b01834d446058a59a8e2025-01-02T17:02:26ZengRiga Technical University PressElectrical, Control and Communication Engineering2255-91592022-06-01181505610.2478/ecce-2022-0007A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A ReviewRauf Abdul0Irshad Muhammad Jehanzeb1Wasif Muhammad2Mehmood Zubair3Kiren Tayybah4Siddique Nazam5Researcher, University of Gujrat, Gujrat, Punjab, PakistanLecturer, University of Gujrat, Gujrat, Punjab, PakistanAssistant Professor, University of Gujrat, Gujrat, Punjab, PakistanLecturer, University of Gujrat, Gujrat, Punjab, PakistanAssistant Professor, University of Engineering and Technology, Lahore, Punjab, PakistanAssistant Professor, University of Gujrat, Gujrat, Punjab, PakistanFor the past decade, the main problem that has attracted researchers’ attention in aerial robotics is the position estimation or Simultaneous Localization and Mapping (SLAM) of Unmanned Aerial Vehicles (UAVs) where the GPS signal is poor or denied. This article reviews the strengths and weaknesses of existing methods in the field of aerial robotics. There are many different techniques and algorithms that are used to overcome the localization and mapping problem of these UAVs. These techniques and algorithms use different sensors, such as Red Green Blue-Depth (RGB_D), Light Detecting and Ranging (LIDAR), and Ultra-wideband (UWB). The most common technique is used, i.e., probability-based SLAM, which uses two algorithms: Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF). LKF consists of five phases and this algorithm is just used for linear system problems. However, the EKF algorithm is used for non-linear systems. Aerial robots are used to perform many tasks, such as rescue, transportation, search, control, monitoring, and different military operations because of their vast top view. These properties are increasing their demand as compared to human service. In this paper, different techniques for the localization of aerial vehicles are discussed in terms of advantages and disadvantages, practicality and efficiency. This paper enables future researchers to find the suitable SLAM solution based on their problems; either the researcher is dealing with a linear problem or a non-linear problem.https://doi.org/10.2478/ecce-2022-0007ekfextended kalman filterlight detecting and rangelinear kalman filtersimultaneously localization and mappingslamunmanned aerial vehicle |
| spellingShingle | Rauf Abdul Irshad Muhammad Jehanzeb Wasif Muhammad Mehmood Zubair Kiren Tayybah Siddique Nazam A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review Electrical, Control and Communication Engineering ekf extended kalman filter light detecting and range linear kalman filter simultaneously localization and mapping slam unmanned aerial vehicle |
| title | A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review |
| title_full | A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review |
| title_fullStr | A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review |
| title_full_unstemmed | A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review |
| title_short | A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review |
| title_sort | state of the art in simultaneous localization and mapping slam for unmanned ariel vehicle uav a review |
| topic | ekf extended kalman filter light detecting and range linear kalman filter simultaneously localization and mapping slam unmanned aerial vehicle |
| url | https://doi.org/10.2478/ecce-2022-0007 |
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