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SVO
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VIO
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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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This code implements a semi-direct monocular visual odometry pipeline which has been integrated with a EKF to infused IMU and CMD as well. The feature extraction and image alignment part are 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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#### Documentation
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OpenCL 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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#### Instructions
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Please be sure that you have the OpenCL driver installed
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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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## 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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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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CPU_version/vio/param/vo_fast.yaml
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# the GPU_version
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# the GPU_version
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Dose not have dependency with GTSAM, the dependencies are:
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The dependencies are:
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1. OpenCV 3
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1. OpenCV 4
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2. Eigen
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2. Eigen
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3. Sophus https://github.com/strasdat/Sophus.git
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3. G2o # build g2o with -DG2O_HAVE_OPENGL=ON -DBUILD_WITH_MARCH_NATIVE=ON
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# G2o version => https://github.com/RainerKuemmerle/g2o/tree/memory_management
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4. Boost
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4. Boost
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5. OpenCL
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5. OpenCL
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if make fails in the build of Sophus with the error lvalue required as left operand of assignment in so2.cpp, see this issue: https://github.com/uzh-rpg/rpg_svo/issues/237
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# known issues
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1. The estimated oriantetion is not accurate
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2. There is a scale map issue in the algorithm
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3. The number of matched points are increasing a lot we need to limit them in a way that will not cause some error in the estimated odometry
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# Log fiels
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log files will be written in the project folder, you can change the path in the cmake files as well as activating debug mode or not
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https://github.com/Gfuse/vio_svo/blob/d3c857fd06bfe5c6180e6bc914a4a36a649c4292/GPU_version/vio/CMakeLists.txt#L89
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