Positioning of UWB and IMU with error state Kalman filter
Indoor-positioning has proved to be an important problem in recent years, because of the increasing urban environment complexity and unavailability of GPS indoors. Recently Ultra Wideband (UWB) have proved to be an emerging promising technology to solve this problem. As Ultra Wideband (UWB) have...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/150354 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Indoor-positioning has proved to be an important problem in recent years, because of
the increasing urban environment complexity and unavailability of GPS indoors. Recently
Ultra Wideband (UWB) have proved to be an emerging promising technology to solve this
problem. As Ultra Wideband (UWB) have high latency, previous work have incorporated
Inertial Measurement Unit (IMU) sensor fusion to mitigate this shortcoming.
Most
popular fusion algorithm to use is based on Extended Kalman Filter, and Unscented
Kalman Filter. However, long duration of usage will have some reduced precision, as there
are bias drifts for accelerometer and gyroscope measurement.
A recent paper by Liu Et Al. is addressing this problem. Which uses Error State
Kalman Filter to do online bias estimation of the IMU. Other than that, tightly coupled
attitude determination is available with ESKF, with no need to rely on other algorithms
like Mahony Filter for example.
The author wants to reenact the experiment by Liu et al. to demonstrate the feasibility
of such methods. |
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