In this paper, we present a technique for the construction of a camera sensor model for visual SLAM. The proposed method is an extension of the general camera calibration procedure and requires the camera to observe a planar checkerboard pattern shown at different orientations. By iteratively placing the pattern at different distances from the camera, we can find a relationship between the measurement noise covariance matrix and the range. We conclude that the error distribution of a camera sensor follows a Gaussian distribution, based on the Geary?s test, and the magnitude of the error variance is linearly related to the range between the camera and the features being observed. Our sensor model can potentially benefit visual SLAM algorithms by varying its measurement noise covariance matrix with range.