Camera Perspective Distortion in Model-based Visual Localisation

This thesis starts with a proposal for a collaborative global visual localisation system. Then, it centres in a specific visual localisation problem: perspective distortion in template matching. The thesis enriches 3D point cloud models with a surface normal associated with each 3D point. These normals are computed using a minimisation algorithm.

Based in this new model, the thesis proposes an algorithm to increase the accuracy of visual localisation. The algorithm improves for template matching processes using surface normals.

The hypothesis, ' Given a 3D point cloud, surface orientation of the 3D points in a template matching process increases the number of inliers points found by the localisation system, that is, perspective compensation.' is objectively proved using a ground truth model.

The ground truth is achieved through the design of a framework which using computer vision and computer graphics techniques carries out experiments without the noise of a real system, and prove in an objective way the hypothesis.