The combination of laser range finder and camera has proven to be very useful in the fields of robotics, mapping, and autonomous vehicles for producing maps and capturing information about the color and texture of objects. In order to fuse data from these two sensors, they need to be calibrated with high accuracy, meaning that their translation vector and rotation matrix with respect to each other must be determined. This paper proposes a method for calibrating a 2D lidar with a camera using the same 3D test fields commonly used in photogrammetric tasks such as camera calibration. In the proposed method, a point-on-plane and an additional constraint are used to obtain the calibration parameters, which is similar to using a chessboard pattern in previous research. To implement and evaluate the proposed method, a system consisting of a 2D laser range finder, a stereo camera, and a servo motor was designed and built. This method was compared with one of the most accurate new calibration methods which used a photogrammetric test field and ping-pong balls. The advantage of the proposed method over this method is the lack of the need for ping-pong balls and the selection of the corresponding points in the point cloud, which is a manual and time-consuming process. Additionally, the proposed method was compared with the method used by Jia fen et al., which uses a pyramid structure created in the corner of a room for calibration. Finally, the calibration parameters of laser range finder with respect to the camera were calculated using the proposed method, the calibration method using a cloud of 3D points, and the J. Fan et al. method. The RMSE of the check points for these three methods was 24.14, 40.12, and 94.15 millimeters, respectively.