PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs
This paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by fe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4241651 |
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| _version_ | 1832567697656774656 |
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| author | Roberto Opromolla Giancarmine Fasano Michele Grassi Al Savvaris Antonio Moccia |
| author_facet | Roberto Opromolla Giancarmine Fasano Michele Grassi Al Savvaris Antonio Moccia |
| author_sort | Roberto Opromolla |
| collection | DOAJ |
| description | This paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map. The innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation. Instead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on least squares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results demonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load and accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of the solutions proposed for line-based mapping and localization-aiding, is evaluated exploiting real range data acquired in indoor environments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned vehicles moving in complex 2D areas, for example, being unexplored, full of obstacles, GPS-challenging, or denied. |
| format | Article |
| id | doaj-art-f73987b237b648e9b10d584e4383f49c |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-f73987b237b648e9b10d584e4383f49c2025-02-03T01:00:52ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742017-01-01201710.1155/2017/42416514241651PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVsRoberto Opromolla0Giancarmine Fasano1Michele Grassi2Al Savvaris3Antonio Moccia4Department of Industrial Engineering, University of Naples “Federico II”, P.le Tecchio 80, 80125 Napoli, ItalyDepartment of Industrial Engineering, University of Naples “Federico II”, P.le Tecchio 80, 80125 Napoli, ItalyDepartment of Industrial Engineering, University of Naples “Federico II”, P.le Tecchio 80, 80125 Napoli, ItalySchool of Aerospace Transport and Manufacturing, Cranfield University, College Rd, Cranfield MK43 0AL, UKDepartment of Industrial Engineering, University of Naples “Federico II”, P.le Tecchio 80, 80125 Napoli, ItalyThis paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map. The innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation. Instead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on least squares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results demonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load and accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of the solutions proposed for line-based mapping and localization-aiding, is evaluated exploiting real range data acquired in indoor environments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned vehicles moving in complex 2D areas, for example, being unexplored, full of obstacles, GPS-challenging, or denied.http://dx.doi.org/10.1155/2017/4241651 |
| spellingShingle | Roberto Opromolla Giancarmine Fasano Michele Grassi Al Savvaris Antonio Moccia PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs International Journal of Aerospace Engineering |
| title | PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs |
| title_full | PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs |
| title_fullStr | PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs |
| title_full_unstemmed | PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs |
| title_short | PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs |
| title_sort | pca based line detection from range data for mapping and localization aiding of uavs |
| url | http://dx.doi.org/10.1155/2017/4241651 |
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