Studies on obstacle detection and path planning for a quadrotor system
Autonomous systems are one interesting topic recently investigated; for land and aerial vehicles; however, the main limitation of aerial vehicles is the weight to carry on-board, since the power consumed depends on this and hardware like sensors and processor is limited. The present thesis develo...
| Autor Principal: | Valencia Mamani, Dalthon Abel |
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| Formato: | Tesis de Maestría |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Materias: | |
| Acceso en línea: |
http://tesis.pucp.edu.pe/repositorio/handle/123456789/9660 |
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| Sumario: |
Autonomous systems are one interesting topic recently investigated; for land and aerial
vehicles; however, the main limitation of aerial vehicles is the weight to carry on-board,
since the power consumed depends on this and hardware like sensors and processor
is limited. The present thesis develops an application of digital image processing to
detect obstacles using only a monocamera, there are some approaches but the present
report wants to focus on the distance estimation approach that, in future works, can
be combined with other methods since this approach is more general.
The distance estimation approach uses feature detection algorithms in two consecutive
images, matching them and thus estimate the obstacle position. The estimation
is computed through a mathematical model of the camera and projections between
those two images. There are many parameters to improve final results and the best
parameters are found and tested with consecutive images, which were captured every
0.5m along a straight path of 5m. Fraunhofer position modules are tested with the
entire algorithm. Finally, in order to establish the new path without obstacles, an optimal
binary integer programming problem is proposed, adapting the approach using
results obtained from the distance estimation and obstacle detection. Resulting data
is suitable for combining them with information obtained from conventional sensors,
such as ultrasonic sensors. The obtained mean error is between 1% and 12% in short
distances (less than 2.5 m) and greater with longer distances.
The complexity of this study lies in the use of a single camera for the capture of
frontal images and obtaining 3D information of the environment, the computation of
the obstacle detection algorithm is tested off-line and the path-planning algorithm is
proposed with detected keypoints in the background. |
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