In this paper, an online calibration method for monocular camera based on point and line features detection is to solve the problem of gross error in the initial parameters of the camera. Firstly, the ORB algorithm and LSD algorithm were used to detect the point and line features respectively, and the initial matching points and lines were matched by FLANN algorithm. Then, the position and attitude of the cameras were calculated based on the geometric relationship between the two views. Finally, the least squares optimization method is used to calculate the inner parameters and distortion coefficients of  the camera. Aiming at low texture environment, such as wall, road and other artificial structures, a joint detection strategy of point-line features is proposed to increase the matching accuracy of two adjacent image features. For the camera calibration method that only uses the point features of the images, the distortion coefficients calculated by the feature points often cannot represent the distortion coefficients of the whole images. In order to further improve the accuracy of the distortion coefficients, the images are divided into pieces by well-proportioned cutting algorithm and the key points are extracted by using non-maximum suppression respectively. Experimental results show that the online camera calibration method proposed has high accuracy in natural environment, and provide the  inner parameters and distortion coefficients of the camera acquired by vision measurement system.