论文标题
最佳姿势估计和协方差分析以及同时定位和映射应用
Optimal Pose Estimation and Covariance Analysis with Simultaneous Localization and Mapping Applications
论文作者
论文摘要
这项工作提供了一种理论分析，可利用地标特征的矢量观察最小二乘，以最佳解决姿势估计问题，这对于涉及同时定位和映射的应用至关重要。首先，通过从点云特征提取的观察向量来制定优化过程。然后，得出了错误协方表达式。事实证明，通过派生优化过程获得的态度和位置估计值可以达到由态度误差小角度近似下的Cramér-Rao下限定义的边界。假定填充的观察噪声量矩阵是成本函数中的重量，以涵盖传感器不确定性的最一般情况。与以前的情况相比，这包括误差中更多的通用相关性，涉及各向同性噪声假设。使用Monte Carlo模拟验证了所提出的解决方案，并进行了实际激光雷达的实验，以验证误差分析。
This work provides a theoretical analysis for optimally solving the pose estimation problem using total least squares for vector observations from landmark features, which is central to applications involving simultaneous localization and mapping. First, the optimization process is formulated with observation vectors extracted from point-cloud features. Then, error-covariance expressions are derived. The attitude and position estimates obtained via the derived optimization process are proven to reach the bounds defined by the Cramér-Rao lower bound under the small-angle approximation of attitude errors. A fully populated observation noise-covariance matrix is assumed as the weight in the cost function to cover the most general case of the sensor uncertainty. This includes more generic correlations in the errors than previous cases involving an isotropic noise assumption. The proposed solution is verified using Monte Carlo simulations and an experiment with an actual LIDAR to validate the error-covariance analysis.
