Implementing different steps to estimate the 3D motion of the camera. Provides as output a plot of the trajectory of the camera.
Visual Odometry is a crucial concept in Robotics Perception for estimating the trajectory of the robot (the camera on the robot to be precise).
This projects aims at implementing different steps to estimate the 3D motion of the camera, and provides as output a plot of the trajectory of the camera.
Frames of a driving sequence taken by a camera in a car, and the scripts to extract the intrinsic parameters are given here.
Comparison of the plots calculated using inbuilt opencv functions (in blue) and by estimating F and E matrix without using the inbuilt functions (in red):
Output video can be found here
Go to directory: cd Code/
To pre-process the images:
To the camera motion estimation task (implementation without OpenCV functions):
You need to add the processed frames in the 'processed_data/frames' directory. Or, you can add the raw frames to './Oxford_dataset/stereo/centre/' dir
To estimate the camera motion using inbuilt functions:
The accuracy of the motion depended upon the number of iteration for the RANSAC algo and the approximated recovery of the pose.
The algorithm sometimes plotted differnt tracks with same tuning parameters.
We experimented with the different combinations of parameters and implementations.
To check whether our implementation calculated appropriate essential matrix we used OpenCV's pose recovery method. We got fair results in this case.
Non-linear methods were consuming more time.