52 lines
1.7 KiB
Markdown
Executable File
52 lines
1.7 KiB
Markdown
Executable File
SVO
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===
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This code implements a semi-direct monocular visual odometry pipeline which has been integrated with GTSAM to infused IMU as well. The feature extraction part is implemented in OpenCL to take advantage of GPU.
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Paper svo: http://rpg.ifi.uzh.ch/docs/ICRA14_Forster.pdf
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Paper Svo+GTSAM: https://arxiv.org/abs/1512.02363
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#### Disclaimer
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SVO has been tested under ROS Groovy, Hydro and Indigo with Ubuntu 12.04, 13.04 and 14.04. This is research code, any fitness for a particular purpose is disclaimed.
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#### Licence
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The source code is released under a GPLv3 licence. A closed-source professional edition is available for commercial purposes. In this case, please contact the authors for further info.
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#### Citing
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If you use SVO in an academic context, please cite the following publication:
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@inproceedings{Forster2014ICRA,
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author = {Forster, Christian and Pizzoli, Matia and Scaramuzza, Davide},
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title = {{SVO}: Fast Semi-Direct Monocular Visual Odometry},
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booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
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year = {2014}
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}
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#### Documentation
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OpenCL documentation
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The API is documented here: http://uzh-rpg.github.io/rpg_svo/doc/
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The GTSAM documentation https://gtsam.org/tutorials/
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#### Instructions
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Please be sure that you have the OpenCL driver installed
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First install gtsam library => https://github.com/borglab/gtsam
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Then follow the SVO instalation
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See the Wiki for more instructions. https://github.com/uzh-rpg/rpg_svo/wiki and https://gtsam.org/get_started/
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## Setting
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You can edit the algorithm parameters in the follwoing file, please look at the config file to underestand the meaning of the parameters (CPU_version/svo/include/svo/config.h)
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CPU_version/vio_svo/param/vo_fast.yaml
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