Error Propagation in Monocular Navigation for Zinf Compared to Eightpoint Algorithm

Abstract

Efficient visual pose estimation plays an important role for a variety of applications. To improve the quality, the measurements from different sensors can be fused. However, a reliable fusion requires the knowledge of the uncertainty of each estimate. In this work, we provide an error analysis for the Zinf algorithm. Furthermore, we extend the existing first-order error propagation for the 8-point algorithm to allow for feature normalization, as proposed by Hartley or Mühlich, and the rotation matrix based decomposition. Both methods are efficient visual odometry techniques which allow high frame-rates and, thus, dynamic motions in unbounded workspaces. Finally, we provide experiments which validate the accuracy of the error propagation and which enable a brief comparison, showing that the Zinf significantly outperforms the 8-point algorithm. We also discuss the influence of the number of features, the aperture angle, and the image resolution on the accuracy of the pose estimation.