An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter
文献类型：期刊论文
| 作者 | Yuwen, Xuan3; Chen, Lu2; Chen, Long1 ; Zhang, Hui3
|
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| 出版日期 | 2023-10-26 |
| 页码 | 10 |
| 关键词 | Active simultaneous localization and mapping (SLAM) outlier detection and correction robust adaptive unscented Kalman filter (RAUKF) visual odometry (VO) |
| ISSN号 | 0278-0046 |
| DOI | 10.1109/TIE.2023.3323730 |
| 通讯作者 | Zhang, Hui(huizhang285@buaa.edu.cn) |
| 英文摘要 | For active visual odometry (VO), the visual information detected by the positioning camera matters. By actively controlling the gaze of the camera, the VO tends to retrieve some effective factors, such as textured objects with rich feature points. However, the active rotation of the camera can introduce more uncertainties, which may cause additional gross errors. Therefore, it is a considerable problem for the active VO to avoid the adverse effects of the active rotation while benefiting from it. To address the issue, this article proposes an improved strategy based on a robust adaptive unscented Kalman filter (RAUKF) and the relative posture of the active camera for the active VO. The pose outputted from the VO is transformed to the vehicle pose by means of the pose of the pan-tilt, and the transformed pose is treated as the measurement of the vehicle motion. Subsequently, the measurement is fed to the RAUKF to generate a refined estimation of the vehicle pose, which is then inversely transformed to obtain a more precise camera pose. Finally, the feature points cloud of the VO can be corrected according to the refined camera pose. The proposed method effectively improves the positioning accuracy of the active VO, as demonstrated through numerical and real-vehicle tests. The relative translation error and the relative rotation error of the proposed method are 1.6% and 0.0037 deg/m in average, which reduce 96.22% and 94.79% compared with the raw outputs of the active VO. |
| WOS关键词 | GAZE CONTROL |
| 资助项目 | Defense Industrial Technology Development Program[JCKY2020601B012] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001092393900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Defense Industrial Technology Development Program |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54295]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Hui |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 102206, Peoples R China 3.Beihang Univ, Sch Transportat Sci & Engn, Beijing 102206, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuwen, Xuan,Chen, Lu,Chen, Long,et al. An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2023:10. |
| APA | Yuwen, Xuan,Chen, Lu,Chen, Long,&Zhang, Hui.(2023).An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,10. |
| MLA | Yuwen, Xuan,et al."An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023):10. |
入库方式： OAI收割
来源：自动化研究所
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