Embedded Tightly Coupled INS/DGPS-DGAL Navigation Filter on a Mass-Market Single-Board Computer

Nitsch, Maximilian; Gehrt, Jan-Jöran; Heyn, Lukas; Zweigel, René; Abel, Dirk

doi:10.1109/icl-gnss51451.2021.9452196

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…The navigation filter uses pseudoranges and deltaranges from GPS and Galileo from a single antenna. In a preprocessing step, differential corrections are calculated using pseudoranges from a reference station received via a modem, implemented in Nitsch et al (2021). If no correction data is available (e.g., during network connection outages), the correction method is replaced by dual-frequency methods or model-based methods, explained in more detail in Gehrt et al (2018).…”

Section: Gnss Preprocessingmentioning

confidence: 99%

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Benz

Gehrt

Zweigel

et al. 2023

navi

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The automation of mining haulage vehicles has great potential in terms of safety and economy. The performance of autonomous vehicles depends largely on highly accurate vehicle state information. Deep mines are especially challenging, as satellite-based localization methods are reaching their limits. Therefore, we introduce a new navigation filter concept for the precise and robust localization of the haulage fleet that can handle temporary GNSS interruptions in deep open-pit mines. The multi-sensor navigation filter utilizes an inertial measurement unit and is aided by GNSS. We introduce a new optical speed sensor update within the tightly coupled unscented Kalman filter. The speed sensor measures the slip-free two-dimensional speed above ground. The filter was validated with an articulated dumper in a gravel pit. The new filter achieved a mean position error of 0.24 m during a test drive of 190 s with a simulated GNSS outage of 90 s.

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Section: Gnss Preprocessingmentioning

confidence: 99%

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Benz

Gehrt

Zweigel

et al. 2023

navi

Self Cite

View full text Add to dashboard Cite

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Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system

2023

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In autonomous applications for mobility and transport, a high-rate and highly accurate vehicle-state estimation is achieved by fusing measurements of global navigation satellite systems (GNSS) and inertial sensors. The state estimation and its protection-level generation often suffer from satellite-signal disturbances in urban environments and subsequent poor parametrization of the satellite observables. Thus, we propose an innovative scheme involving an extended H∞ filter (EHF) for robust state estimation and zonotope for the protection-level generation. This scheme is shown as part of a tightly coupled navigation system based on an inertial navigation system and aided by the GPS/Galileo dual-constellation satellite navigation system. Specifically, GNSS pseudorange and deltarange observables are utilized. The experimental results of post-processing a real-world dataset show significant advantages of EHF against a conventional extended Kalman filter regarding the navigation accuracy and robustness under various GNSS measurement parametrizations and environmental circumstances. The zonotope-based protection-level calculation is proven valid, computationally affordable, and feasible for real-time implementations.

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Accuracy Evaluation of a Low-Cost Differential Global Positioning System for Mobile Robotics

Blesing,

Finke,

Hoose

et al. 2023

2023 Ieee Sensors

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Embedded Tightly Coupled INS/DGPS-DGAL Navigation Filter on a Mass-Market Single-Board Computer

Cited by 4 publications

References 15 publications

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system

Accuracy Evaluation of a Low-Cost Differential Global Positioning System for Mobile Robotics

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