Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 616 69 Brno, Czech Republic.
Independent Researcher, 74 401 Frenštát pod Radhoštěm, Czech Republic.
Sensors (Basel). 2023 Jun 20;23(12):5746. doi: 10.3390/s23125746.
Planar fiducial markers are commonly used to estimate a pose of a camera relative to the marker. This information can be combined with other sensor data to provide a global or local position estimate of the system in the environment using a state estimator such as the Kalman filter. To achieve accurate estimates, the observation noise covariance matrix must be properly configured to reflect the sensor output's characteristics. However, the observation noise of the pose obtained from planar fiducial markers varies across the measurement range and this fact needs to be taken into account during the sensor fusion to provide a reliable estimate. In this work, we present experimental measurements of the fiducial markers in real and simulation scenarios for 2D pose estimation. Based on these measurements, we propose analytical functions that approximate the variances of pose estimates. We demonstrate the effectiveness of our approach in a 2D robot localisation experiment, where we present a method for estimating covariance model parameters based on user measurements and a technique for fusing pose estimates from multiple markers.
平面基准标记通常用于估计相机相对于标记的姿势。此信息可以与其他传感器数据结合使用，使用状态估计器（如卡尔曼滤波器）为系统在环境中的全局或局部位置提供估计。为了获得准确的估计，必须正确配置观测噪声协方差矩阵，以反映传感器输出的特征。然而，从平面基准标记获得的姿势的观测噪声在测量范围内变化，在进行传感器融合以提供可靠的估计时需要考虑到这一点。在这项工作中，我们针对 2D 姿势估计在真实和模拟场景中展示了基准标记的实验测量结果。基于这些测量结果，我们提出了近似姿势估计方差的分析函数。我们在 2D 机器人定位实验中展示了我们方法的有效性，其中我们提出了一种基于用户测量值估计协方差模型参数的方法，以及一种融合多个标记的姿势估计的技术。
