OpenNavSense platform: A low-cost, open-source inertial navigation system for the evaluation of estimation algorithms
Inertial navigation systems (INS) are widely used in commercial aviation, maritime navigation, and unmanned vehicle guidance. However, these systems are often sensitive, costly, and challenging to access. To address these limitations, an open-source, low-cost platform named INS OpenNavSense has been...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | HardwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468067224001159 |
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| Summary: | Inertial navigation systems (INS) are widely used in commercial aviation, maritime navigation, and unmanned vehicle guidance. However, these systems are often sensitive, costly, and challenging to access. To address these limitations, an open-source, low-cost platform named INS OpenNavSense has been developed. This platform is built using FreeRTOS, an open-source real-time operating system (RTOS) that enables the microcontroller to run parallel individual threads (tasks), providing a practical and effective tool for implementing estimation algorithms that compensate for the use of low-cost microelectromechanical systems (MEMS) sensors instead of high-end sensors in professional INS. The main contribution of this work is the introduction of a FreeRTOS-based platform, which facilitates independent management of computational and processing tasks. The platform incorporates accelerometers, gyroscopes, magnetometers, Global Positioning System (GPS) module, and barometer sensors. Sensor data is calibrated and filtered to enhance accuracy, offering researchers a robust and reliable tool for testing their estimation algorithms. To validate this platform, the open-source Mahony library was used for attitude and heading reference system estimation, demonstrating the types of algorithms that can be tested. Tests were conducted with a drone carrying the platform as payload, and results from this low-cost INS were compared to the drone’s INS, showing both similarity and viability as a development platform. |
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| ISSN: | 2468-0672 |