Occupancy grid based urban localization using weighted point cloud

Localization is considered as a key capability for autonomous vehicles act in urban environments. Though have been proved to be able to perform convictive results, localization methods using neither laser scanners nor vision sensors could achieve the goal about balancing between accuracy and cost. In this paper, an occupancy grid based localization framework is presented in order to obtain a precise positioning result with relatively low-cost sensor configuration in large scale urban environment. The proposed approach takes a prebuilt global grid map as prior knowledge for localization. Model based feature extraction method is introduced to provide laser points classification, with each extracted point allocated a specified weight to describe local characteristic. The prior grid map is generated from weighted point cloud to be able to describe the local metric features such as curbs and building facades. Localization function is then carried out with a weight point based maximum likelihood matching method to determine the correspondence between local point cloud and the reference grid map. There are also position initialization and reference map management modules to make the framework more practical and reliable. In the end, the proposed approach has been validated by promising experimental results with long distance tests in large urban environments.