| Author: | Li, Junxi |
| Title: | UVIO : Gaussian-Kalman filtering UWB aided visual-inertial SLAM system for complex indoor environments |
| Degree: | M.Sc. |
| Year: | 2023 |
| Department: | Department of Mechanical Engineering |
| Pages: | v, 82 pages : color illustrations |
| Language: | English |
| Abstract: | Considering the complex indoor environment, which is a challenging task for GPS, it is unlikely to receive a strong and reliable GPS signal. For existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most ideal solutions. In this paper, we propose a fusion method of ultra-wideband (UWB), Inertial Measurement Unit(IMU), and camera simultaneous localization and mapping (SLAM) to achieve sufficient accuracy and robustness for the control of vehicles in an indoor environment. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In this paper, the measurement distance data of UWB and the acceleration information of IMU are merged by adaptive Kalman filter, and a state-of-art factor graph optimization(FGO) method was proposed to tightly integrate the UWB-IMU measurements (LOS and corrected measurements) with camera SLAM. According to previous work, we found a strong correlation between Received Signal Strength (RSS) and position. A RSS and EOP factor-related gain was proposed to adjust the covariance matrix and dilution of precision (DOP) of each base station, in order to achieve more agile accuracy feedback. This feedback is finally taken consider by Kalman Gain. The experimental results show that compared with a single or dual positioning system, the proposed data fusion method can significantly improve positioning accuracy. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7472.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 13.13 MB | Adobe PDF | View/Open |
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