A Novel Real-Time Reference Key Frame Scan Matching Method
Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach usi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/5/1060 |
| _version_ | 1811308381324967936 |
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| author | Haytham Mohamed Adel Moussa Mohamed Elhabiby Naser El-Sheimy Abu Sesay |
| author_facet | Haytham Mohamed Adel Moussa Mohamed Elhabiby Naser El-Sheimy Abu Sesay |
| author_sort | Haytham Mohamed |
| collection | DOAJ |
| description | Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems. |
| first_indexed | 2024-04-13T09:22:10Z |
| format | Article |
| id | doaj.art-744ca3a6103a4af8b5c2238031bad7a3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:22:10Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-744ca3a6103a4af8b5c2238031bad7a32022-12-22T02:52:34ZengMDPI AGSensors1424-82202017-05-01175106010.3390/s17051060s17051060A Novel Real-Time Reference Key Frame Scan Matching MethodHaytham Mohamed0Adel Moussa1Mohamed Elhabiby2Naser El-Sheimy3Abu Sesay4Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaPublic Works Department, Ain Shams University, Cairo 11566, EgyptDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaUnmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.http://www.mdpi.com/1424-8220/17/5/1060scan matchingSLAMlaser range finderpoint registrationleast squaresline trackingPCAICPUAVkey frame |
| spellingShingle | Haytham Mohamed Adel Moussa Mohamed Elhabiby Naser El-Sheimy Abu Sesay A Novel Real-Time Reference Key Frame Scan Matching Method Sensors scan matching SLAM laser range finder point registration least squares line tracking PCA ICP UAV key frame |
| title | A Novel Real-Time Reference Key Frame Scan Matching Method |
| title_full | A Novel Real-Time Reference Key Frame Scan Matching Method |
| title_fullStr | A Novel Real-Time Reference Key Frame Scan Matching Method |
| title_full_unstemmed | A Novel Real-Time Reference Key Frame Scan Matching Method |
| title_short | A Novel Real-Time Reference Key Frame Scan Matching Method |
| title_sort | novel real time reference key frame scan matching method |
| topic | scan matching SLAM laser range finder point registration least squares line tracking PCA ICP UAV key frame |
| url | http://www.mdpi.com/1424-8220/17/5/1060 |
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