Intelligent vehicles use a lidar-camera sensor fusion system to perceive the environment. Two calibration methods, feature point method and checkerboard method, are proposed for the joint calibration of different sensor coordinate systems in the data fusion. The feature-point method employs a tailored calibration template to extract several pairs of corresponding points, and solves the constraint equations for the calibration parameters in virtue of the least square method. The checkerboard method employs Zhang’s calibration method to obtain the intrinsic parameters of the camera. And then, equations are derived by using the consistency of the checkerboard plane in lidar and camera coordinate systems, solving the extrinsic parameters between the two coordinates using a linear method. The result is further refined by a nonlinear optimization method. The lidar points are projected onto the image plane by using the calibration results obtained from the two methods. Experiments demonstrate that the two methods are capable of obtaining the accurate position parameters between the coordinate systems of each sensor. The projection alignment errors are 3.03 pixels for the feature point method and 2.33 pixels for the checkerboard method.