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Unmanned Aerial Vehicle Positioning Algorithm Based on the Secant Slope Characteristics of Transmission Lines

机译:基于传输线截面特性的无人空中车辆定位算法

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摘要

Aiming at the problem of positioning consistency and real-time performance during power inspection of unmanned aerial vehicles (UAV), this paper proposes an UAV positioning algorithm based on the secant slope characteristics of transmission lines. The traditional correlation scan matching (CSM) positioning method is prone to mismatch under the condition of a single geometric feature. Although the inertial measurement unit (IMU) -aided CSM method improves the positioning accuracy, using the IMU for a long time to estimate the position is prone to cumulative errors and increases the computational cost to a certain extent. Our proposed method uses the least square polynomial curve fitting method to parametric reconstruction of the transmission line, which enhances the geometric characteristics of the transmission line and made up the missing part. The secant line of transmission line is extracted and its feature is parameterized to match with the reconstructed transmission line map, and the translation vector and rotation vector of UAV are estimated. Through multiple sets of experiments, it is proved that our algorithm is less than the CSM method and the IMU-aided CSM method in terms of average translation error, average rotation error, and positioning time. Our algorithm not only improves the positioning accuracy but also guarantees the real-time positioning requirements.
机译:旨在在无人机(UAV)电力检查期间定位一致性和实时性能的问题,本文提出了一种基于传输线的截面特性的UAV定位算法。传统的相关扫描匹配(CSM)定位方法在单个几何特征的条件下容易匹配。尽管惯性测量单元(IMU) - 求解的CSM方法提高了定位精度,但是使用IMU长时间估计位置容易累积误差并在一定程度上提高计算成本。我们所提出的方法利用最小二乘多项式曲线拟合方法来传输线的参数重建,这增强了传输线的几何特性并构成了缺失部分。提取剪切线的分传输线,并且其特征被参数化以与重建的传输线映射匹配,并且估计UAV的平移矢量和旋转向量。通过多组实验,证明我们的算法小于CSM方法和IMU - 辅助CSM方法,在平均转换误差,平均旋转误差和定位时间方面。我们的算法不仅可以提高定位精度,而且还可以保证实时定位要求。

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