Pose estimation for an autonomous vehicle using monocular vision

Vision-based pose estimation is particularly important for small sized mobile robots and aerial vehicles that have limited payload constraints. Accurate localization is of importance for any autonomous vehicle, especially in indoor and GPS-denied (Global Positioning System) environment. The aim of this work is to provide a solution for the pose estimation of an autonomous vehicle using monocular vision alone so that even a small-size vehicle can perform pose estimation without the help of additional sensors. Unlike Visual Odometry which operates on a sequence of images to estimate robot's motion, the proposed method estimates the robot's pose using individual images by constantly tracking four fixed feature points in a rectangular pattern, whose positions are known a priori. Also, unlike Monocular Visual Odometry which suffers from scale ambiguity problem, the proposed algorithm can estimate the pose of the vehicle and can be applied to both planar robots (3-DoF) as well as aerial vehicles (6-DoF). The proposed method has been validated by implementing on a Raspberry Pi2 model B and a RPi camera. Simulation results of controlling a differential drive robot using the proposed pose estimation method are also presented.