LiDAR is a high-accuracy remote sensor method that uses laser light to measure ranges and it can be employed to indoor navigation and environment detection for robots and AGVs. Simultaneous localization and mapping（ SLAM） is a common way of integration of Li DAR and MEMS INS. A new integration scheme of Li DAR and a MEMS INS is proposed. The environment features extracting from the measurements of Li DAR and feature map construction are optimized. The relative position parameters derived from features matching algorithm are used to estimate the error of MEMS INS by a Kalman filter. A prototype is designed to verify the practicability of the integrated navigation system of Li DAR and MEMS INS Some experiments are carried out in a room and the results demonstrate the potential use of the LiDAR SLAM/MEMS INS integration navigation system.