Monocular Vision-inertial Fusion Approach for MAV State Estimation
To improve the pose estimation accuracy of quadrotors MAV(mertial measurement unit), it presented a new algorithm of state estimation for quadrotors. In this paper, a modular Kalman filter framework was designed, which combines the initial pose estimation of the vision part and the IMU-based dynamic...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2018-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.009 |
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| _version_ | 1849224768551649280 |
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| author | RU Xiangyu JIN Chao PAN Chengfeng XU Chao |
| author_facet | RU Xiangyu JIN Chao PAN Chengfeng XU Chao |
| author_sort | RU Xiangyu |
| collection | DOAJ |
| description | To improve the pose estimation accuracy of quadrotors MAV(mertial measurement unit), it presented a new algorithm of state estimation for quadrotors. In this paper, a modular Kalman filter framework was designed, which combines the initial pose estimation of the vision part and the IMU-based dynamic model, and this framework is a new visual inertial odometry. The ARTags were used to obtain pose estimation, and the result was used as the observation of the algorithm. The dynamic model based on IMU was established, and the visual result was added to the state vector for iterations. Since the frequency of IMU is higher than that of the visual result, the most recent state vector is selected when it updates. The algorithm still works well when the visual result is abnormal, and it shows a good performance in the experiment. |
| format | Article |
| id | doaj-art-efcb7fe60231496c956fe03075b2a38a |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-efcb7fe60231496c956fe03075b2a38a2025-08-25T06:55:20ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272018-01-0135505882331831Monocular Vision-inertial Fusion Approach for MAV State EstimationRU XiangyuJIN ChaoPAN ChengfengXU ChaoTo improve the pose estimation accuracy of quadrotors MAV(mertial measurement unit), it presented a new algorithm of state estimation for quadrotors. In this paper, a modular Kalman filter framework was designed, which combines the initial pose estimation of the vision part and the IMU-based dynamic model, and this framework is a new visual inertial odometry. The ARTags were used to obtain pose estimation, and the result was used as the observation of the algorithm. The dynamic model based on IMU was established, and the visual result was added to the state vector for iterations. Since the frequency of IMU is higher than that of the visual result, the most recent state vector is selected when it updates. The algorithm still works well when the visual result is abnormal, and it shows a good performance in the experiment.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.009quadrotorpose estimationIMU(inertial measurement unit)ARTagKalman filter |
| spellingShingle | RU Xiangyu JIN Chao PAN Chengfeng XU Chao Monocular Vision-inertial Fusion Approach for MAV State Estimation Kongzhi Yu Xinxi Jishu quadrotor pose estimation IMU(inertial measurement unit) ARTag Kalman filter |
| title | Monocular Vision-inertial Fusion Approach for MAV State Estimation |
| title_full | Monocular Vision-inertial Fusion Approach for MAV State Estimation |
| title_fullStr | Monocular Vision-inertial Fusion Approach for MAV State Estimation |
| title_full_unstemmed | Monocular Vision-inertial Fusion Approach for MAV State Estimation |
| title_short | Monocular Vision-inertial Fusion Approach for MAV State Estimation |
| title_sort | monocular vision inertial fusion approach for mav state estimation |
| topic | quadrotor pose estimation IMU(inertial measurement unit) ARTag Kalman filter |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.009 |
| work_keys_str_mv | AT ruxiangyu monocularvisioninertialfusionapproachformavstateestimation AT jinchao monocularvisioninertialfusionapproachformavstateestimation AT panchengfeng monocularvisioninertialfusionapproachformavstateestimation AT xuchao monocularvisioninertialfusionapproachformavstateestimation |