COMPARISON BETWEEN RGB AND RGB-D CAMERAS FOR SUPPORTING LOW-COST GNSS URBAN NAVIGATION
| Autor: | Lorenzo Rossi, C. I. De Gaetani, Mirko Reguzzoni, Livio Pinto, Diana Pagliari, Eugenio Realini |
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| Rok vydání: | 2018 |
| Předmět: |
lcsh:Applied optics. Photonics
0209 industrial biotechnology 010504 meteorology & atmospheric sciences Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology lcsh:Technology 01 natural sciences law.invention Extended Kalman filter 020901 industrial engineering & automation Data acquisition law Low-cost Focal length Computer vision 0105 earth and related environmental sciences Urban Navigation Kinect GNSS lcsh:T business.industry Orientation (computer vision) lcsh:TA1501-1820 RGB-D Kalman filter Filter (signal processing) Relative Orientation Lens (optics) lcsh:TA1-2040 GNSS applications RGB color model Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business Focus (optics) |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2, Pp 991-998 (2018) |
| ISSN: | 2194-9034 |
| Popis: | A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15 % better than the one obtained when using the Canon EOS M camera. |
| Databáze: | OpenAIRE |
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