Path planning for unmanned vehicle motion based on road detection using online road map and satellite image

Abstract

This article presents a new methodology for detecting road network and planning the path for vehicle motion using road map and satellite/aerial images. The method estimates road regions from based on network models, which are created from road maps and satellite images on the basis of using image-processing techniques such color filters, difference of Gaussian, and Radon transform. In the case of using the road map images, this method can estimate not only a shape but also a direction of road network, which would not be estimated by the use of the satellite images. However, there are some road segments that branch from the main road are not annotated in road map services. Therefore, it is necessary to detect roads on the satellite image, which is utilized to construct a full path for motion. The scheme of method includes several stages. First, a road network is detected using the road map images, which are collected from online maps services. Second, the detected road network is used to learn a model for road detection in the satellite images. The road network using the satellite images is estimated based on filter models and geometry road structures. Third, the road regions are converted into a Mercator coordinate system and a heuristic based on Dijkstra technique is used to provide the shortest path for vehicle motion. This methodology is tested on the large scene of outdoor areas and the results are documented.