Underwater vehicle motion parameters estimation simulation and experiment based on monocular vision and low cost inertial measurement unit
文献类型：会议论文
| 作者 | Li Q(李强); Zhang QF(张奇峰) ; Wang XH(王晓辉)
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| 出版日期 | 2009 |
| 会议名称 | 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE |
| 会议日期 | June 21-26, 2009 |
| 会议地点 | Osaka, Japan |
| 关键词 | Air filters Autonomous underwater vehicles Calibration Data fusion Experiments Inertial navigation systems Information fusion Motion estimation Nonlinear analysis Nonlinear filtering Submersibles |
| 页码 | 730-737 |
| 中文摘要 | In this paper, authors proposed a practical motion estimation strategy to obtain the single static object position and the vehicle's motion parameters simultaneously by utilizing camera and IMU (Inertial Measurement Unit), and tried several classic nonlinear parameters estimation methods on this problem based on the Matlab simulation. A lot of preliminary calibration experiments have been done to fuse the data from IMU and camera, which included: camera calibration, IMU error modeling, and relative attitude calibration between IMU and camera. The obtaining of sensor's model made the simulation work be possible, and the PF algorithm can track the vehicle's motion parameters and obtain the target position very well. The accuracy is centimeter level. |
| 收录类别 | EI |
| 产权排序 | 1 |
| 会议主办者 | International Society of Offshore and Polar Engineers, ISOPE |
| 会议录 | Proceedings of the International Offshore and Polar Engineering Conference
 |
| 会议录出版者 | International Society of Offshore and Polar Engineers |
| 会议录出版地 | Cupertino, CA |
| 语种 | 英语 |
| ISSN号 | 1098-6189 |
| ISBN号 | 978-1-88065-353-1 |
| 源URL | [http://ir.sia.cn/handle/173321/7858]  |
| 专题 | 沈阳自动化研究所_水下机器人研究室 |
| 推荐引用方式 GB/T 7714 | Li Q,Zhang QF,Wang XH. Underwater vehicle motion parameters estimation simulation and experiment based on monocular vision and low cost inertial measurement unit[C]. 见:19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE. Osaka, Japan. June 21-26, 2009. |
入库方式： OAI收割
来源：沈阳自动化研究所
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