Parameter and Configuration Analysis for Non-linear Pose Estimation with Points and Lines

In markerless model-based tracking approaches image features as points or straight lines are used to estimate the pose. We introduce an analysis of parametrizations of the pose data as well as of error measurements between 2D image features and 3D model data. Further, we give a review of critical geometrical configurations as they can appear on the input data. From these results the best parameter choice for a non-linear pose estimator is proposed that is optimal by construction to handle a combined input of feature correspondences and works on an arbitrary number and choice of feature type. It uses the knowledge of the 3D model to analyze the input data for critical geometrical configurations.