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Vehicle Localization and Classification Using Off-Board Vision and 3-D Models.
- Source :
-
IEEE Transactions on Robotics . Apr2014, Vol. 30 Issue 2, p432-447. 16p. - Publication Year :
- 2014
-
Abstract
- Vehicle localization is one of the most common tasks in robotics. Using vision as a sensor, most methods perform localization from cameras mounted on board the vehicle. In contrast, we propose a method based on an off-board camera. The system uses a similarity measure between a camera image and a synthetic image generated using a 3-D vehicle model, ideally converging to the true pose of the vehicle. The similarity measure is based on a model of shading appearance, which depends on the surface curvature of the 3-D model. Considering that the area observed by the camera is fairly planar, a rough initial estimation for the position of the object can be obtained using 2-D blob tracking. The 3-D model of the vehicle can be rendered near the vehicle in the real image, and using the proposed similarity measure, it converges to the correct pose. A classification function to discriminate between different vehicles is also proposed, making it possible for the system to identify and track multiple vehicles of interest. A number of experiments are performed with different vehicles outdoors, in a real industrial environment, considering different illumination conditions. The low average error rate compared with a laser-based ground truth illustrates the applicability of the method. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 15523098
- Volume :
- 30
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Robotics
- Publication Type :
- Academic Journal
- Accession number :
- 95433215
- Full Text :
- https://doi.org/10.1109/TRO.2013.2291613