akhopkar01/visual-odometry-sfm

visual-odometry-sfm

This is the repository for the project - Camera pose tracking using visual odometry

---------- Our Implementation ---------------------------------------- Opencv Implementation -----------

Video with camera mounted on the car

System and library requirements.

• Python3
• Numpy
• cv2
• math
• glob
• matplotlib

How to Run

1. Clone this repo or extract the "nalindas_proj_5.zip" file.
   
2. Navigate to the folder "Code"
   
3. Inside the python script - DataPreprocess.py, you need to specify the appropriate original dataset path in the file_path variable. Next, you need to specify the appropriate camera model parameters path in the camera_path variable. Finally, you need to specify the path where you want the processed undistorted images to be saved in the write_path variable.
4. Inside the python script - main.py, you need to specify the processed undistorted dataset path which you had specified in DataPreprocess.py in the path variable. Next, you need to specify the appropriate camera model parameters path in the camera_path variable. Finally, you need to specify the path where you want the camera pose plot images to be saved in the save_path variable.
5. In the terminal, run the command python main.py to run our implementation. Run the command python builtin_main.py to run the opencv implementation. (You need to specify the appropriate original dataset path in the IMAGES_PATH variable. Finally, you need to specify the path where you want the camera pose plot images to be saved in the MODELS_PATH variable.)