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+VIO
+===
+
+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.
+
+    
+#### Documentation
+OpenCL documentation
+
+#### Instructions
+Please be sure that you have the OpenCL driver installed
+
+
+## Setting
+
+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)
+
+CPU_version/vio/param/vo_fast.yaml
+
+# The dependencies:
+1. OpenCV 4
+2. Eigen
+3. G2o  # build g2o with -DG2O_HAVE_OPENGL=ON -DBUILD_WITH_MARCH_NATIVE=ON
+        # G2o version => https://github.com/RainerKuemmerle/g2o/tree/memory_management
+4. Boost
+5. OpenCL
+
+# known issues
+    1. The estimated oriantetion is not accurate 
+    2. There is a scale map issue in the algorithm 
+    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
+    
+# Log files
+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
+https://github.com/Gfuse/vio_svo/blob/d3c857fd06bfe5c6180e6bc914a4a36a649c4292/GPU_version/vio/CMakeLists.txt#L89
+
+
+