Aiming at the problem of LIDAR and camera data fusion， a calibration method of 2D LIDAR and color camera based on indoor structural features was proposed. According to the position relationship between the point and lines， pillar features of the indoor scene are used to derive the projection matrix equation between the 2D LIDAR and the camera coordinate systems. The Canny operator and Hough transform are employed to extract the line features of the corner image. Afterwards， the RANSAC method is used to fit the corner features from the point cloud. The projection matrix is solved by the singular value decomposition method. Finally， the calibration results are further optimized after removing the data with large reprojection errors. The experimental results show that the average reprojection error of the pillar feature point is reduced from 0.375 5 pixels to 0.045 9 pixels by adopting optimized projection matrix. Compared with the two-step calibration method， the algorithm proposed in this paper has a better reprojection effect. At the same time， this method does not require a specific calibration object， and can achieve high projection accuracy in less time.