UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes
With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5862 |
| _version_ | 1797412223435407360 |
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| author | Jun Dai Songlin Liu Xiangyang Hao Zongbin Ren Xiao Yang |
| author_facet | Jun Dai Songlin Liu Xiangyang Hao Zongbin Ren Xiao Yang |
| author_sort | Jun Dai |
| collection | DOAJ |
| description | With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. |
| first_indexed | 2024-03-09T04:59:33Z |
| format | Article |
| id | doaj.art-de809619d7574a28a3646e432c2f573a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:33Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-de809619d7574a28a3646e432c2f573a2023-12-03T13:02:15ZengMDPI AGSensors1424-82202022-08-012215586210.3390/s22155862UAV Localization Algorithm Based on Factor Graph Optimization in Complex ScenesJun Dai0Songlin Liu1Xiangyang Hao2Zongbin Ren3Xiao Yang4Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, ChinaDengzhou Water Conservancy Bureau, Dengzhou 474150, ChinaWith the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations.https://www.mdpi.com/1424-8220/22/15/5862UAVmulti-source fusionfactor graph optimizationrobustness |
| spellingShingle | Jun Dai Songlin Liu Xiangyang Hao Zongbin Ren Xiao Yang UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes Sensors UAV multi-source fusion factor graph optimization robustness |
| title | UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes |
| title_full | UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes |
| title_fullStr | UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes |
| title_full_unstemmed | UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes |
| title_short | UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes |
| title_sort | uav localization algorithm based on factor graph optimization in complex scenes |
| topic | UAV multi-source fusion factor graph optimization robustness |
| url | https://www.mdpi.com/1424-8220/22/15/5862 |
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