| 孙晨阳* **,张群莉*,潘聪* **,邵兵兵**,方灶军**.基于因子图的多传感器融合定位方法[J].高技术通讯(中文),2024,34(4):413~419 |
| 基于因子图的多传感器融合定位方法 |
| Multi-sensor fusion localization method based on factor graph |
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| DOI：10. 3772 / j. issn. 1002-0470. 2024. 04. 009 |
| 中文关键词: 移动机器人； 因子图优化； 紧耦合； 惯性测量单元（IMU）预积分 |
| 英文关键词: mobile robot, factor graph optimization, tightly-coupled, inertial measurement unit (IMU) pre-integration |
| 基金项目: |
| 作者 | 单位 | | 孙晨阳* ** | (*浙江工业大学机械工程学院杭州 310014)
(**中国科学院宁波材料技术与工程研究所宁波 315201) | | 张群莉* | | | 潘聪* ** | | | 邵兵兵** | | | 方灶军** | |
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| 中文摘要: |
| 针对移动机器人在室内环境中使用单一传感器或松耦合定位存在定位精度低和鲁棒性不足的问题，提出一种基于因子图的多传感器紧耦合定位算法。该算法分别接收来自惯性测量单元（IMU）、轮式编码器和2D激光雷达的数据，并构建IMU预积分因子、轮式里程计因子、位姿先验因子以及激光里程计因子；通过因子图对这些因子进行增量优化后输出得到移动机器人的状态信息，同时实时估计IMU的漂移量并进行校正。实验结果表明，无论是在未知环境还是已知环境下，该定位算法都可以有效提高移动机器人的定位精度和鲁棒性。 |
| 英文摘要: |
| Aiming at the problems of low localization accuracy and insufficient robustness of mobile robots using single sensor or loosely coupled localization in indoor environment, a multi-sensor tightly coupled localization algorithm based on factor graph is proposed. The algorithm receives data from inertial measurement unit (IMU), wheel encoder and 2D lidar, and constructs IMU pre-integration factor, wheel odometry factor, pose prior factor and laser odometry factor. The state information of the mobile robot is obtained after incremental optimization of these factors through the factor graph, and the drift of the IMU is estimated and corrected in real time. Experimental results show that the localization algorithm can effectively improve the localization accuracy and robustness of mobile robots in unknown or known environments. |
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