Vehicle Ego-Motion Estimation by using Pulse-Coupled Neural Network

Abstract

This paper presents a visual odometer system using a monocular camera for vehicle navigation. A novel algorithm for vehicle ego-motion estimation based on optical flow and image segmentation is proposed. By applying a pulse-coupled neural network (PCNN), the image is dynamically divided into road area and non-road area by analysing texture smoothness. Correct road region detection effectively reduces computation cost and improves accuracy of ego-motion estimation. Then a novel optical flow optimization method is proposed to produce reliable optical flow field in the road area detected previously. It's known when the vehicle is moving on a planar structured road, its 2D motion field is expected to have specific form. Therefore ego-motion of vehicle, instantaneous speed and angular velocity, can be recovered from optical flow field of road area. Experiments show that the visual odometer successfully provides driver with robust and accurate vehicle self motion information.