Incremental Hopping-Window Pose-Graph Fusion for Real-Time Vehicle Localization

Abstract

In this work, we research and evaluate incremental hopping-window pose-graph fusion strategies for vehicle localization. Pose-graphs can model multiple absolute and relative vehicle localization sensors, and can be optimized using non-linear techniques. We focus on the performance of incremental hopping-window optimization for on- line usage in vehicles and compare it with global off-line optimization. Our evaluation is based on 180 Km long vehicle trajectories that are recorded in highway, urban, and rural areas, and that are accompanied with post-processed Real Time Kinematic GNSS as ground truth. The results exhibit a 17% reduction in the error's standard deviation and a significant reduction in GNSS outliers when compared with automotive-grade GNSS receivers. The incremental hopping-window pose- graph optimization bounds the computation cost, when compared to global pose-graph fusion, which increases linearly with the size of the pose- graph, whereas the difference in accuracy is only 1%. This allows real-time usage of non-linear pose-graph fusion for vehicle localization.