Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the vis...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/11507 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846106156366299136 |
|---|---|
| author | Ao Liu Hang Guo Min Yu Jian Xiong Huiyang Liu Pengfei Xie |
| author_facet | Ao Liu Hang Guo Min Yu Jian Xiong Huiyang Liu Pengfei Xie |
| author_sort | Ao Liu |
| collection | DOAJ |
| description | The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the GNSS/IMU integrated positioning data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning. |
| format | Article |
| id | doaj-art-b6e214863c4e4d7cbc4e9d119e78da6b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b6e214863c4e4d7cbc4e9d119e78da6b2024-12-27T14:07:29ZengMDPI AGApplied Sciences2076-34172024-12-0114241150710.3390/app142411507Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive FilteringAo Liu0Hang Guo1Min Yu2Jian Xiong3Huiyang Liu4Pengfei Xie5School of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaCollege of Computer Software, Jiangxi Normal University, Nanchang 330022, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaThe accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the GNSS/IMU integrated positioning data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning.https://www.mdpi.com/2076-3417/14/24/11507GNSSIMUvisualadaptive filteringposition |
| spellingShingle | Ao Liu Hang Guo Min Yu Jian Xiong Huiyang Liu Pengfei Xie Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering Applied Sciences GNSS IMU visual adaptive filtering position |
| title | Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering |
| title_full | Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering |
| title_fullStr | Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering |
| title_full_unstemmed | Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering |
| title_short | Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering |
| title_sort | research on gnss imu visual fusion positioning based on adaptive filtering |
| topic | GNSS IMU visual adaptive filtering position |
| url | https://www.mdpi.com/2076-3417/14/24/11507 |
| work_keys_str_mv | AT aoliu researchongnssimuvisualfusionpositioningbasedonadaptivefiltering AT hangguo researchongnssimuvisualfusionpositioningbasedonadaptivefiltering AT minyu researchongnssimuvisualfusionpositioningbasedonadaptivefiltering AT jianxiong researchongnssimuvisualfusionpositioningbasedonadaptivefiltering AT huiyangliu researchongnssimuvisualfusionpositioningbasedonadaptivefiltering AT pengfeixie researchongnssimuvisualfusionpositioningbasedonadaptivefiltering |