Accurate Localization of Autonomous Vehicles Based on Pattern Matching and Graph-Based Optimization in Urban Environments
Accurate and reliable localization is a prerequisite for autonomous driving. Methods based on sparse landmarks, such as pole-like structures, have been widely studied because of their lower requirements for computing and storage. However, the number of landmarks of a single type is not always sufficient for reliable positioning. We propose a localization method using three different types of features in urban environments. The features we choose are poles, corners and walls which are persistent over time and can be reliably detected with LiDAR sensors. A pattern matching method for data association is introduced. Instead of using a filtering method, we adopt the graph-based optimization method to solve the pose estimation problem. Experiments conducted on two test roads show that the proposed method can provide accurate and reliable localization results in urban environments.