A Real-Time Pose Estimation Algorithm Based on FPGA and Sensor Fusion
A Real-Time Pose Estimation Algorithm Based on FPGA and Sensor Fusion
复制标题
一种基于FPGA和传感器融合的实时姿态估计算法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Szilveszter Pletl
中科院分区：
文献类型：
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作者：
Laszlo Schäffer;Zoltán Kincses;Szilveszter Pletl
组合不同传感器的测量是在姿势估计中获得更好精度的关键步骤。传感器融合是一种有效的状态估计方法（在这种情况下为Kalman滤波器），该方法用于多个学科。使用传感器融合，可以将来自传感器的信息和每个传感器的特性一起使用，以改善估计值并减少测量变量的不确定性。在本文中，提出了使用视觉探光（光流），惯性测量单元（IMU）和全局定位系统（GPS）测量的传感器融合的实时姿势估计算法。 IMU包含经过校准的三个自由度（3DOF）加速度计和3DOF陀螺仪。 Kalman滤波器用于融合不同传感器的测量。该算法使用Zybo开发板在MATLAB和低成本Z-7010田间可编程栅极阵列（FPGA）中实现，该板能够通过传感器融合进行实时姿势估算。
Combining measurements of different sensors are a crucial step to achieve better precision in pose estimation. Sensor fusion is an effective state estimation method (in this case Kalman filter), which is used in several disciplines. Using sensor fusion, the information from the sensors and the characteristics of each sensor can be used together to improve the estimate and decrease the uncertainty of the measured variables. In this paper a real-time pose estimation algorithm using sensor fusion of visual odometry (optical flow), Inertial Measurement Unit (IMU) and Global Positioning System (GPS) measurements is presented. The IMU contains calibrated three degrees of freedom (3Dof) accelerometer and an also 3DoF gyroscope. A Kalman filter is used for the fusion of the measurements of the different sensors. The algorithm is implemented in MATLAB and on a low-cost Z-7010 Field-Programmable Gate Array (FPGA) using the ZYBO development board, which is capable of real-time pose estimation with sensor fusion.