A calibration method of intrinsic parameters was proposed, which aimed at optimizing both the flatness and the area of planar patches detected from the point cloud. Either region growing algorithm or improved Random Sample Consensus (RANSAC) algorithm was chosen to extract planes according to different cases. A hybrid optimization algorithm (i.e. NMS-ABC) integrating the Nelder-Mead Simplex algorithm with the Artificial Bees Colony algorithm was proposed to calculate parameters, by means of which the intrinsic calibration of the DIY system was successfully implemented. Simulation experiments were performed using synthetic data based on known intrinsic parameters, which demonstrated the efficiency of NMS-ABC hybrid algorithm. Actual calibration experiments were conducted with multiple groups of point cloud data acquired in different scenes. Results indicate that both the percentage of planar inliers and the total flatness are improved after calibration. Besides, the calibration results will be more stable and reliable when the number of detected planes is not less than 3. Position precision test was conducted, and position precision of the DIY scanner was improved to the level of 3 mm@4 m after calibration. By increasing installation errors artificially and then contrasting point coulds as well as extracted planes before and after calibration, it is visually validated that the proposed calibration method is correct and effective.