SLAM问题中特征相关性研究

结合SLAM算法及不确定性分析,对SLAM问题中的特征相关性进行了研究.并在对相关性进行详细深入分析的基础上,得到了特征稀疏的两个标准,进而提出了相关优先的特征稀疏策略,可利用较少的相关性强的特征从而减少大量的计算负担,计算误差却和一般传统方法相当.最后,采用EKF滤波对SLAM进行了仿真,通过多次Monte-Carlo仿真实验结果表明了该方法的有效性.

Correlation between Features in Simultaneous Localization and Map Building (SLAM) Problem

The correlation problem was studied based on SLAM algorithm and uncertainty analysis. It's well-known that the correlation between features is actually the critical part of the SLAM problem. Maintaining and renewing this correlation information brings a huge computation burden. Therefore, on the basis of having carried out deep analysis on correlation, a new feature sparse tactic named correlation priority was brought forward, which may use less features having strong correlation to cut down large amount of the computation burden, and the computation error of this method can compare with that of some general traditional methods. Finally, the SLAM algorithm was simulated by EKF and the simulation results indicate that this method is valid.