How to obtain sparse information matrix is the key factor for information filtering（ IF） simultaneous localization and mapping（ IFSLAM） algorithm to solve filtering performance. By deep analysis on sparse extended information filtering（ SEIF）

finding that it ’s difficult for sparse result to guarantee the weakest relevance environment active characteristics of being thinning after sparse process; in order to improve this defect

an improved SLAM algorithm based on entropy sparse rules is proposed

it uses the entropy property and considering the current and next observing moment to select posture correlating the weakest environment characteristic as the sparse feature points; effectively improve the sparse performance of the algorithm. The improved algorithm is simulated on Matlab

and validity is verified.