Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera
Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6752 |
| _version_ | 1797546739203309568 |
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| author | Lingxiao Zheng Xingqun Zhan Xin Zhang |
| author_facet | Lingxiao Zheng Xingqun Zhan Xin Zhang |
| author_sort | Lingxiao Zheng |
| collection | DOAJ |
| description | Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity. |
| first_indexed | 2024-03-10T14:33:30Z |
| format | Article |
| id | doaj.art-e8178c1548284bbeaad242472002013d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:33:30Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e8178c1548284bbeaad242472002013d2023-11-20T22:23:45ZengMDPI AGSensors1424-82202020-11-012023675210.3390/s20236752Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a CameraLingxiao Zheng0Xingqun Zhan1Xin Zhang2School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaUsing a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.https://www.mdpi.com/1424-8220/20/23/6752consumer electronicsattitude estimationinertial sensorscameranonlinear complementary filter |
| spellingShingle | Lingxiao Zheng Xingqun Zhan Xin Zhang Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera Sensors consumer electronics attitude estimation inertial sensors camera nonlinear complementary filter |
| title | Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera |
| title_full | Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera |
| title_fullStr | Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera |
| title_full_unstemmed | Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera |
| title_short | Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera |
| title_sort | nonlinear complementary filter for attitude estimation by fusing inertial sensors and a camera |
| topic | consumer electronics attitude estimation inertial sensors camera nonlinear complementary filter |
| url | https://www.mdpi.com/1424-8220/20/23/6752 |
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