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Assignment Brief

This assignment is designed to give you an insight into selected aspects of computer vision applied to camera calibration, visual odometry, and structure from motion, i.e., camera pose and orientation estimation from a sequence of images taken by that camera. You are asked to solve various tasks including detection of image keypoints, their robust matching, camera pose estimation, and correction of the camera pose drift error. You are asked to write a computer vision software operating in a soft real-time as well as testing your solution and interpreting the results.

This assignment will enable you to:

• Deepen your understanding of camera calibration, keypoints detection / matching, homography, fundamental matrix, and camera pose estimation.
• Recognize software design challenges behind implementations of computer vision algorithms.
• Design and optimise software to meet specified requirements.
• Acquire a hands-on understanding of camera calibration and simultaneous localisation and mapping problems.

(These correspond to point 1, 2, 4 and 5 of the module learning outcomes. Module learning outcomes are provided in the Module Descriptor)

The assignment consists of two main tasks. The first task is to perform camera calibration using images stored in the CalibrationImages_MVO.zip file. These calibration images were captured with a checkerboard calibration pattern placed at different positions and orientations. The size of the checkerboard square is 14.44mm x 14.44mm.
The second task is to estimate three-dimensional camera poses (position & orientation) for the sequence of images from the CVML Monocular Visual Odometry dataset stored in the CVML_MVO_Loop.zip file.These images were captured with varying camera position and orientation. The images in both the CalibrationImages_MVO and CVML_MVO_Loop were taken by the same camera. You are asked to write matlab programs to estimate intrinsic camera parameters using data in the CalibrationImages_MVO.zip file and subsequently estimate the camera pose for each corresponding image in the CVML_MVO_Loop.zip sequence.

In visual odometry, an estimate of the global pose of the camera for the current frame tends to drift from the true pose due to matching errors between consecutive frames. If camera trajectory loops, shown the same part of the scene as before, this can be used to correct some of the camera pose drift errors. You to implement algorithm for such “loop closure”.

It is essential that you design your camera pose estimation algorithm, so it can be used in a sequential manner, i.e., when estimating the current camera pose only the current and preceding images can be used.

The CalibrationImages_MVO_Loop.zip and CVML_MVO_Loop.zip files are available from Blackboard EL3105 Assignment space.