Li Huixia, Wen Xi, Guo Hang, Yu Min
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China.
School of Resources Environment & Chemical Engineering, Ministry of Education Key Laboratory of Poyang Lake Environment and Resource Utilization, Nanchang University, Nanchang 330031, China.
Sensors (Basel). 2018 Mar 12;18(3):839. doi: 10.3390/s18030839.
As indoor mobile navigation suffers from low positioning accuracy and accumulation error, we carried out research into an integrated location system for a robot based on Kinect and an Inertial Measurement Unit (IMU). In this paper, the close-range stereo images are used to calculate the attitude information and the translation amount of the adjacent positions of the robot by means of the absolute orientation algorithm, for improving the calculation accuracy of the robot's movement. Relying on the Kinect visual measurement and the strap-down IMU devices, we also use Kalman filtering to obtain the errors of the position and attitude outputs, in order to seek the optimal estimation and correct the errors. Experimental results show that the proposed method is able to improve the positioning accuracy and stability of the indoor mobile robot.
由于室内移动导航存在定位精度低和累积误差的问题，我们开展了基于Kinect和惯性测量单元（IMU）的机器人集成定位系统研究。本文利用近距离立体图像，通过绝对定向算法计算机器人相邻位置的姿态信息和平移量，以提高机器人运动的计算精度。依靠Kinect视觉测量和捷联式IMU设备，我们还使用卡尔曼滤波来获取位置和姿态输出的误差，以寻求最优估计并校正误差。实验结果表明，所提方法能够提高室内移动机器人的定位精度和稳定性。
