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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Multi-sensor fusion is an effective way to enhance the positioningperformance of autonomous underwater vehicles (AUVs). However, underwatermulti-sensor fusion faces challenges such as heterogeneous frequency anddynamic availability of sensors. Traditional filter-based algorithms sufferfrom low accuracy and robustness when sensors become unavailable. The factorgraph optimization (FGO) can enable multi-sensor plug-and-play despite datafrequency. Therefore, we present an FGO-based strapdown inertial navigationsystem (SINS) and long baseline location (LBL) system tightly couplednavigation system (FGO-ILNS). Sensors such as Doppler velocity log (DVL),magnetic compass pilot (MCP), pressure sensor (PS), and global navigationsatellite system (GNSS) can be tightly coupled with FGO-ILNS to satisfydifferent navigation scenarios. In this system, we propose a floating LBL slantrange difference factor model tightly coupled with IMU preintegration factor toachieve unification of global position above and below water. Furthermore, toaddress the issue of sensor measurements not being synchronized with the LBLduring fusion, we employ forward-backward IMU preintegration to constructsensor factors such as GNSS and DVL. Moreover, we utilize the marginalizationmethod to reduce the computational load of factor graph optimization.Simulation and public KAIST dataset experiments have verified that, compared tofilter-based algorithms like the extended Kalman filter and federal Kalmanfilter, as well as the state-of-the-art optimization-based algorithm ORB-SLAM3,our proposed FGO-ILNS leads in accuracy and robustness.

FGO-ILNS: Tightly Coupled Multi-Sensor Integrated Navigation System Based on Factor Graph Optimization for Autonomous Underwater Vehicle