| 基于改进克隆卡尔曼滤波的视觉惯性里程计实现 |
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| 引用本文： | 徐之航,欧阳威,武元新. 基于改进克隆卡尔曼滤波的视觉惯性里程计实现[J]. 信息技术, 2020, 0(5): 21-27,36 |
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| 作者姓名： | 徐之航 欧阳威 武元新 |
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| 作者单位： | 上海交通大学上海市北斗导航与位置服务重点实验室 |
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| 基金项目： | 国家自然科学基金(61673263)。 |
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| 摘 要： | 视觉-惯性多传感器融合方案可以有效解决GNSS据止环境下的导航问题。为了更有效地处理视觉的相对观测信息,文中研究并实现了一种基于克隆卡尔曼滤波(stochastic cloning Kalman Filter,SCKF)与滑动窗方法相结合的低成本视觉惯性里程计紧耦合算法。开源数据集实验表明,文中算法精度超过了多状态约束卡尔曼滤波(MSCKF)开源代码的效果。
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| 关 键 词： | 惯性视觉里程计 克隆卡尔曼滤波 滑动窗 多状态约束 |
Implement of visual-inertial odometry based on improved stochastic cloning kalman filtering |
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| XU Zhi-hang,OUYANG Wei,WU Yuan-xin. Implement of visual-inertial odometry based on improved stochastic cloning kalman filtering[J]. Information Technology, 2020, 0(5): 21-27,36 |
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| Authors: | XU Zhi-hang OUYANG Wei WU Yuan-xin |
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| Affiliation: | (Shanghai Key Laboratory of Navigation and Location-based Services,Shanghai Jiaotong University,Shanghai 200240,China) |
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| Abstract: | Kalman filter based visual-inertial multi-sensor fusion scheme can effectively solve the navigation problem in GNSS-denied environment.In order to deal with the relative measurement,a low-cost,sliding-window tight-coupled algorithm of visual-inertial odometry is researched and implemented based on the stochastic cloning Kalman Filter(SCKF).Experiments with open source datasets show that the proposed algorithm has better accuracy than the multi-state constraint Kalman filter(MSCKF) open source code. |
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| Keywords: | visual-inertial odometry stochastic cloning Kalman filter sliding-window multi-state constraint |
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