Improved Feature Matching for Mobile Devices with IMU
Thanks to the recent diffusion of low-cost high-resolution digital cameras and to the development of mostly automated procedures for image-based 3D reconstruction, the popularity of photogrammetry for environment surveys is constantly increasing in the last years. Automatic feature matching is an im...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/8/1243 |
| _version_ | 1811184944824713216 |
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| author | Andrea Masiero Antonio Vettore |
| author_facet | Andrea Masiero Antonio Vettore |
| author_sort | Andrea Masiero |
| collection | DOAJ |
| description | Thanks to the recent diffusion of low-cost high-resolution digital cameras and to the development of mostly automated procedures for image-based 3D reconstruction, the popularity of photogrammetry for environment surveys is constantly increasing in the last years. Automatic feature matching is an important step in order to successfully complete the photogrammetric 3D reconstruction: this step is the fundamental basis for the subsequent estimation of the geometry of the scene. This paper reconsiders the feature matching problem when dealing with smart mobile devices (e.g., when using the standard camera embedded in a smartphone as imaging sensor). More specifically, this paper aims at exploiting the information on camera movements provided by the inertial navigation system (INS) in order to make the feature matching step more robust and, possibly, computationally more efficient. First, a revised version of the affine scale-invariant feature transform (ASIFT) is considered: this version reduces the computational complexity of the original ASIFT, while still ensuring an increase of correct feature matches with respect to the SIFT. Furthermore, a new two-step procedure for the estimation of the essential matrix E (and the camera pose) is proposed in order to increase its estimation robustness and computational efficiency. |
| first_indexed | 2024-04-11T13:21:53Z |
| format | Article |
| id | doaj.art-d2a694192ca54e03b9694c09c280b5a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:21:53Z |
| publishDate | 2016-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d2a694192ca54e03b9694c09c280b5a02022-12-22T04:22:11ZengMDPI AGSensors1424-82202016-08-01168124310.3390/s16081243s16081243Improved Feature Matching for Mobile Devices with IMUAndrea Masiero0Antonio Vettore1CIRGEO (Interdepartmental Research Center of Geomatics), University of Padova, via dell’Università 16, 35020 Legnaro (PD), ItalyCIRGEO (Interdepartmental Research Center of Geomatics), University of Padova, via dell’Università 16, 35020 Legnaro (PD), ItalyThanks to the recent diffusion of low-cost high-resolution digital cameras and to the development of mostly automated procedures for image-based 3D reconstruction, the popularity of photogrammetry for environment surveys is constantly increasing in the last years. Automatic feature matching is an important step in order to successfully complete the photogrammetric 3D reconstruction: this step is the fundamental basis for the subsequent estimation of the geometry of the scene. This paper reconsiders the feature matching problem when dealing with smart mobile devices (e.g., when using the standard camera embedded in a smartphone as imaging sensor). More specifically, this paper aims at exploiting the information on camera movements provided by the inertial navigation system (INS) in order to make the feature matching step more robust and, possibly, computationally more efficient. First, a revised version of the affine scale-invariant feature transform (ASIFT) is considered: this version reduces the computational complexity of the original ASIFT, while still ensuring an increase of correct feature matches with respect to the SIFT. Furthermore, a new two-step procedure for the estimation of the essential matrix E (and the camera pose) is proposed in order to increase its estimation robustness and computational efficiency.http://www.mdpi.com/1424-8220/16/8/12433D reconstructionphotogrammetryfeature matchinginertial navigation systemsmartphones |
| spellingShingle | Andrea Masiero Antonio Vettore Improved Feature Matching for Mobile Devices with IMU Sensors 3D reconstruction photogrammetry feature matching inertial navigation system smartphones |
| title | Improved Feature Matching for Mobile Devices with IMU |
| title_full | Improved Feature Matching for Mobile Devices with IMU |
| title_fullStr | Improved Feature Matching for Mobile Devices with IMU |
| title_full_unstemmed | Improved Feature Matching for Mobile Devices with IMU |
| title_short | Improved Feature Matching for Mobile Devices with IMU |
| title_sort | improved feature matching for mobile devices with imu |
| topic | 3D reconstruction photogrammetry feature matching inertial navigation system smartphones |
| url | http://www.mdpi.com/1424-8220/16/8/1243 |
| work_keys_str_mv | AT andreamasiero improvedfeaturematchingformobiledeviceswithimu AT antoniovettore improvedfeaturematchingformobiledeviceswithimu |