EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES
For autonomous navigation of micro aerial vehicles (MAVs), a robust detection of obstacles with onboard sensors is necessary in order to avoid collisions. Cameras have the potential to perceive the surroundings of MAVs for the reconstruction of their 3D structure. We equipped our MAV with two fishey...
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2015-08-01
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Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-dfb6285632dc43ffbfeb6f2674580d8b2020-11-25T01:06:23ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502015-08-01II-1/W1334010.5194/isprsannals-II-1-W1-33-2015EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSESN. Krombach0D. Droeschel1S. Behnke2Autonomous Intelligent Systems Group, Institute for Computer Science VI, University of Bonn, Bonn, GermanyAutonomous Intelligent Systems Group, Institute for Computer Science VI, University of Bonn, Bonn, GermanyAutonomous Intelligent Systems Group, Institute for Computer Science VI, University of Bonn, Bonn, GermanyFor autonomous navigation of micro aerial vehicles (MAVs), a robust detection of obstacles with onboard sensors is necessary in order to avoid collisions. Cameras have the potential to perceive the surroundings of MAVs for the reconstruction of their 3D structure. We equipped our MAV with two fisheye stereo camera pairs to achieve an omnidirectional field-of-view. Most stereo algorithms are designed for the standard pinhole camera model, though. Hence, the distortion effects of the fisheye lenses must be properly modeled and model parameters must be identified by suitable calibration procedures. In this work, we evaluate the use of real-time stereo algorithms for depth reconstruction from fisheye cameras together with different methods for calibration. In our experiments, we focus on obstacles occurring in urban environments that are hard to detect due to their low diameter or homogeneous texture.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-1-W1/33/2015/isprsannals-II-1-W1-33-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N. Krombach D. Droeschel S. Behnke |
spellingShingle |
N. Krombach D. Droeschel S. Behnke EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
N. Krombach D. Droeschel S. Behnke |
author_sort |
N. Krombach |
title |
EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES |
title_short |
EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES |
title_full |
EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES |
title_fullStr |
EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES |
title_full_unstemmed |
EVALUATION OF STEREO ALGORITHMS FOR OBSTACLE DETECTION WITH FISHEYE LENSES |
title_sort |
evaluation of stereo algorithms for obstacle detection with fisheye lenses |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2015-08-01 |
description |
For autonomous navigation of micro aerial vehicles (MAVs), a robust detection of obstacles with onboard sensors is necessary in order to
avoid collisions. Cameras have the potential to perceive the surroundings of MAVs for the reconstruction of their 3D structure. We
equipped our MAV with two fisheye stereo camera pairs to achieve an omnidirectional field-of-view. Most stereo algorithms are designed
for the standard pinhole camera model, though. Hence, the distortion effects of the fisheye lenses must be properly modeled and model
parameters must be identified by suitable calibration procedures. In this work, we evaluate the use of real-time stereo algorithms for
depth reconstruction from fisheye cameras together with different methods for calibration. In our experiments, we focus on obstacles
occurring in urban environments that are hard to detect due to their low diameter or homogeneous texture. |
url |
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-1-W1/33/2015/isprsannals-II-1-W1-33-2015.pdf |
work_keys_str_mv |
AT nkrombach evaluationofstereoalgorithmsforobstacledetectionwithfisheyelenses AT ddroeschel evaluationofstereoalgorithmsforobstacledetectionwithfisheyelenses AT sbehnke evaluationofstereoalgorithmsforobstacledetectionwithfisheyelenses |
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