A Robust Monocular Visual Odometry Algorithm for Autonomous Robot Application

The paper proposes a methodology for reducing the effects of noise in the sequential trajectory reconstruction process based on a monocamera video stream. The recovery of the robot odometry is based on a features tracking technique. In the proposed approach, the estimation of the of ego-motion is improved by exploiting the information extracted from a dynamic window that memorizes the last frames seen by the camera. At each step, the Fundamental Matrix is recovered by selecting, from the dynamic window, the best matching frames ranked according a specific index of performance that is the condition number of the LS problem associated to the Fundamental Matrix computation. The performance of the proposed strategy are analyzed in presence of image quantization noise and in presence of inaccuracy in the feature point matching.