Jiang Pin, Hu Chen, Wang Tingting, Lv Ke, Guo Tingfeng, Jiang Jinxuan, Hu Wenwu
College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China.
Sensors (Basel). 2024 Mar 6;24(5):1710. doi: 10.3390/s24051710.
In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system.
在移动机器人的自主导航中，精确的定位至关重要。在卫星信号微弱的森林环境或受复杂环境干扰的场所，卫星定位精度难以满足机器人自主导航定位精度的要求。本文提出了一种视觉同步定位与地图构建（SLAM）/超宽带（UWB）紧密耦合定位方法，并利用视觉里程计的位移增量设计了一种UWB非视距误差识别方法。它将视觉输出的位移增量和UWB测距信息作为测量值，并应用扩展卡尔曼滤波算法进行数据融合。本研究利用构建的实验平台在室外环境中采集图像和超宽带测距数据，并通过实验验证了组合定位方法。实验结果表明，该算法在定位精度方面优于单独的UWB或松耦合组合定位方法。它有效消除了UWB中的非视距误差，提高了组合定位系统的精度和稳定性。
