JultikaUniversity of Oulu repository

Pose estimation is a well-studied problem in computer vision. Many solutions which provide high accuracy depend on nonlinear optimization. For real-time applications, linear or closed-form solutions are preferred. Some relatively new methods also fuse inertial sensor data with that from the visual sensor to achieve higher accuracy. We propose a closed-form solution to estimate camera pose using two lines and gravity information. The system is developed so that it could work in unprepared environments which satisfy the Manhattan world assumption. We first test the proposed method on a synthetic data set and compare it to other state-of-the-art point and line based pose estimation methods, comparing their mean rotation and mean translation errors. I.M.U. noise effect on the overall performance of the system is also tested. We then proceed to test our algorithm in real world by rectifying perspective deformed images. The deviation of the calculated pose from the ground-truth pose is calculated for each image to test the real world performance of the proposed algorithm. Also, I.M.U. noise is calculated, which correspond to the 0.5% noise level expected in low cost I.M.U.’s.