This paper presents the outcome of an investigation of the costs and benefits of thinning as part of preprocessing for line detection including specification of end-points, from visual images of indoor rectilinear environments. This is done as part of a bigger process with the goal of detecting lines to enable a small mobile robot self-navigate within the environment based on navigationally important features such as doors and corridors reconstructed from the lines detected. The straight line Hough transform is used to determine parameters which specify gradients and positions of lines, and then the end-points of the lines are determined. To do this images can be preprocessed to the point of edge-detection which typically yields edge lines several pixels thick, or edge-detection followed by thinning yielding edge lines about a single pixel in thickness. Since the Hough transform operates on all pixels in an input image, more work is needed to process the "unthinned" image. However, thinning itself takes time. This paper looks into whether the taking the time to do thinning is justified in terms of overall time taken, and quality of resulting lines found, and concludes that for the purpose described, thinning does appear to improve the quality of line detection, while taking less total time to do it.