On the error analysis of vertical line pair-based monocular visual odometry in urban area

When a robot travels in urban area, Global Positional System (GPS) signals might be obstructed by buildings. Hence visual odometry is a choice. We notice that the vertical edges from high buildings and poles of street lights are a very stable set of features that can be easily extracted. Thus, we develop a monocular vision-based odometry system that utilizes the vertical edges from the scene to estimate the robot ego-motion. Since it only takes a single vertical line pair to estimate the robot ego-motion on the road plane, here we model the ego-motion estimation process and analyze how the choice of different vertical line pair impacts the accuracy of the ego-motion estimation process. The resulting closed form error model can assist to choose an appropriate pair of vertical lines to reduce the error in computation. We have implemented the proposed method and validated the error analysis results in physical experiments.