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Visual Odometry (VO) is the process of calculating the motion of an agent (such as, robot and vehicle), using images captured by a single or multiple cameras embedded to it. VO is an important process to supplement autonomous navigation systems, since VO can provide accurate trajectory estimates. However, algorithms of VO work with several steps of hard numerical computation which generate numerical errors and demand considerable processing time. In this paper, we propose the use of a mathematical framework for monocular VO process based on Polynomial Eigenvalue Problem (PEP) modeling in order to achieve both more accurate motion estimation and to decrease the processing time of the VO process. Some previous experiments are shown in order to validate the proposed computation accuracy.

Using Polynomial Eigenvalue Problem Modeling to Improve Visual Odometry for Autonomous Vehicles