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In order to improve the efficiency of grasping objects by service robot in the indoor environment, we present a method for improving the surface normal estimation of point cloud. Real-time and accuracy are the key component of grasping objects by service robot. We get the point cloud by the point cloud by the consumer-level sensor kinectV2. It is proposed to use the integrated image to reduce the time of traversal consumption of pixels in the filter area for improving real-time. Smoothing the point cloud data dynamically adjust the size and assign weights for the smoothing area for improving the accuracy of normal estimation. According to the improved normal direction, the region growing algorithm is used to classify the normal in lines by different directions, and then the planes are extracted from the point cloud. Finally,a comparison of the plane segmentation tests was performed by our methods and the Point Cloud Library. The results show, our method is more accurate and real-time than the Point Cloud Library methods for the plane segmentation with the depth(1-5) meter of point cloud.