Incremental hopping-window pose-graph fusion for real-time vehicle localization

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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.
Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)978-1-7281-1216-9
ISBN (Print)978-1-7281-1217-6
Publication statusPublished - 27 Jun 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019


Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
Abbreviated title VTC Spring 2019
CityKuala Lumpur

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