Map-Based Localization with Factor Graphs for Automated Driving using Non-Semantic LiDAR Features

The knowledge of the map-relative pose of a vehicle is crucial for automated driving. This paper presents a localization approach using a graph-based sliding window optimization with non-semantic LiDAR features. In short, this paper introduces and evaluates our overall concept of non-semantic localization, whose idea and steps were depicted in previous publications. In doing so, we do not rely on static, semantic landmarks, like building corners, thus we achieve independence of these infrastructure elements. Our approach consists of three main steps. At first, we collect the input data, i. e. extracting on-board features from LiDAR point clouds. Afterward, the on-board features are associated over time and matched with the features stored in the map. Then, we use the on-board detected features, map matched features, odometry and GNSS measurements to build a factor graph. The factor graph is optimized to estimate the vehicle pose. Our approach is tested on real-world data. The experiment suggests that our method of applying non-semantic elements provides a working localization solution with practical relevance. It also displays that our approach is persistent examining test drives over one and half years along the same route.