Pose Estimation Based on Monocular Visual Odometry and Lane Detection for Intelligent Vehicles

Abstract

A fundamental element for the determination of the position (pose) of an object is to be able to determine the rotation and translation of the same in space. Visual odometry is the process of determining the location and orientation of a camera by analyzing a sequence of images. The algorithm allowed tracing the trajectory of a body in an open environment by comparing the mapping of points of a sequence of images to determine the variation of translation or rotation. The use of Lane detection is proposed to feed back the Visual Odometry algorithm, allowing more robust results. The algorithm was programmed on OpenCV 3.0 in Python 2.7 and was run on Ubuntu 16.04. The algorithm allowed tracing the trajectory of a body in an open environment by comparing the mapping of points of a sequence of images to determine the variation of translation or rotation. With the satisfactory results obtained, the development of a computational platform capable of determining the position of a vehicle in the space for assistance in parking is projected.