This paper proposes a formulation of quaternion-based Extended Kalman Filter pose estimation for six degrees of freedom systems embedded in an FPGA with commercial processors. Our approach uses the fusion of a camera and an inertial measurement unit to estimate simultaneously the position and the orientation of the system of interest. In addition, a Stewart platform is used to validate and evaluate the estimated pose. Although this work considers the use of common low-cost sensors and the use of markers with simple geometry, the results show excellent performance of the developed filter, being able to estimate the pose and orientation with an error below 8.14 mm and 0.63^o̱, respectively. Furthermore, the effectiveness of the approach has also been evaluated, showing that the filter is able to converge quickly when the markers are retrieved after a loss of camera data for a short period of time.

本文提出了一种针对基于四元数的扩展卡尔曼滤波器姿态估计的公式，该估计用于嵌入具有商用处理器的FPGA中的六个自由度系统。我们的方法使用摄像机和惯性测量单元的融合来同时估计目标系统的位置和方向。另外，使用Stewart平台来验证和评估估计的姿势。虽然这项工作考虑使用普通的低成本传感器和使用简单的几何形状的标记，结果显示所开发的过滤器的性能优良，能与错误估计低于8.14毫米和0.63的姿势和方向^Ø， 分别。此外，还评估了该方法的有效性，表明当在短时间内丢失相机数据后检索到标记时，过滤器能够快速收敛。