Point cloud registration is a key step for indoor mobile robot pose estimation scenario building. Current point cloud registration methods hardly work in lowtexture scenes. To improve scene adaptability of indoor mobile robot, this paper proposes a novel improved 3DNDT point cloud registration algorithm this paper proposes an improved ORB algorithm to ensure valid feature extraction in lowtexture scenes; In addition, to improve accuracy and effectivity of point cloud registration, this paper proposes improved 3DNDT algorithm for quick and accurate registration matrix solving. Quantitative results on famous TUM datasets show our system performs as good as or better than other popular solutions (lower RMSE value than 002 m), and time consuming decreases 3 time than traditional 3DNDT algorithm; Notably, our algorithm can work in low texture scenes. Therefore, our algorithm can improve scene adaptability of indoor mobile robot.