English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate and robust vehicle localization in highly urbanized areas ischallenging. Sensors are often corrupted in those complicated and large-scaleenvironments. This paper introduces GNSS-FGO, an online and global trajectoryestimator that fuses GNSS observations alongside multiple sensor measurementsfor robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensormeasurements into the graph with a continuous-time trajectory representationusing Gaussian process regression. This enables querying states at arbitrarytimestamps so that sensor observations are fused without requiring strict stateand measurement synchronization. Thus, the proposed method presents ageneralized factor graph for multi-sensor fusion. To evaluate and studydifferent GNSS fusion strategies, we fuse GNSS measurements in loose and tightcoupling with a speed sensor, IMU, and lidar-odometry. We employed datasetsfrom measurement campaigns in Aachen, Duesseldorf, and Cologne in experimentalstudies and presented comprehensive discussions on sensor observations,smoother types, and hyperparameter tuning. Our results show that the proposedapproach enables robust trajectory estimation in dense urban areas, where theclassic multi-sensor fusion method fails due to sensor degradation. In a testsequence containing a 17km route through Aachen, the proposed method results ina mean 2D positioning error of 0.19m for loosely coupled GNSS fusion and 0.48mwhile fusing raw GNSS observations with lidar odometry in tight coupling.

GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization