39 lines
1.3 KiB
Markdown
39 lines
1.3 KiB
Markdown
VIO
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===
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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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#### Documentation
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OpenCL documentation
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#### Instructions
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Please be sure that you have the OpenCL driver installed
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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/param/vo_fast.yaml
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# The dependencies:
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1. OpenCV 4
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2. Eigen
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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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5. OpenCL
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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 files
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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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