Hetergeneous Sensor Information Fusion based on Kernel Adaptive Filtering for UAVs
文献类型：会议论文
| 作者 | Zhiheng Chen; Can Wang; Huiguo Wang; Yue Ma; Guoyuan Liang; Xinyu Wu |
| 出版日期 | 2017 |
| 会议地点 | 中国澳门 |
| 英文摘要 | Due to the low weight of monocular camera, monocular Simultaneous Localization and Mapping (SLAM) is an area of popular research and promotes countless applications of micro Unmanned Aerial Vehicles (UAVs), especially in some GPS-denied indoor environments. Nevertheless, the motion of UAVs is often faster and more complex than that of groundbased robots. It would also lead to error accumulation if we calculate the trajectory only through the ego-motion. For purpose of higher accuracy and lower power cost, the fusion of visual and inertial measurement sensors is presented in UAV’s indoor navigation. In this paper, we propose a novel loosely-coupled system to integrate monocular visual odometry (VO) with reading from Inertial navigation system (INS) for UAVs’ indoor localization. We acquire the data from Inertial Measurement Unit (IMU) and VO results individually and map them into a same feature space. The space is defined by the Tensor Product of the individual Kernels for each source. Based on the method of Kernel Adaptive Filtering method-kernel space least mean squares (KLMS), these data are fused in the highdimensional space. Then, experiments are made to verify this method. Compared with the vision-only algorithms, it can be confirmed that the Kernel Adaptive Filtering method makes some improvements in localization accuracy of UAVs. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11877]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Zhiheng Chen,Can Wang,Huiguo Wang,et al. Hetergeneous Sensor Information Fusion based on Kernel Adaptive Filtering for UAVs[C]. 见:. 中国澳门. |
入库方式： OAI收割
来源：深圳先进技术研究院
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