Vehicle state estimation by moving horizon estimation considering occlusion and outlier on 3D static cameras
Vehicle state estimation by moving horizon estimation considering occlusion and outlier on 3D static cameras
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DOI：
10.1109/cca.2015.7320777
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
M. Takahashi;K. Nonaka;K. Sekiguchi
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文献类型：
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作者：
M. Takahashi;K. Nonaka;K. Sekiguchi
使用3D静态相机测量可以实现高精度定位，但应考虑离群值或遮挡。有必要对它们进行补偿以提高精度。为了解决这个问题，我们介绍了滚动时域估计（MHE），并将其与扩展卡尔曼滤波（EKF）进行比较，以评估估计精度。在本文中，我们进行三维静态相机测量的车辆在具有挑战性的条件下，在数值模拟和实验。然后，通过估计的位置和航向角的车辆，估计精度进行了比较，显示的有效性，即使在遮挡图像的状态估计MHE。
Measurement using 3D static cameras can achieve high accuracy localization, but outlier or occlusion should be considered. It is necessary to compensate them to improve accuracy. To address this issue, we introduce Moving Horizon Estimation (MHE) and compare it with Extended Kalman Filter (EKF) to evaluate the estimation accuracy. In this paper, we conduct 3D static camera measurement for a vehicle under challenging conditions in both numerical simulation and experiment. Then, through estimation of position and heading angle of the vehicle, the estimation accuracy is compared to show the effectiveness of the state estimation by MHE even under occlusion of images.