In recent research, autonomous vehicle navigation has been often done by processing visual information. This approach is useful in urban environments, where tall buildings can disturb satellite receiving and GPS localization, while offering numerous and useful visual features. Our vehicle uses a monocular camera, and the path is represented as a series of reference images. Since the robot is equipped with only one camera, it is difficult to guarantee vehicle pose accuracy during navigation. The main contribution of this article is the evaluation and comparison (both in the image and in the 3D pose state space) of six appearance-based controllers (one posebased controller, and five image-based) for replaying the reference path. Experimental results, in a simulated environment, as well as on a real robot, are presented. The experiments show that the two image jacobian controllers, that exploit the epipolar geometry to estimate feature depth, outperform the four other controllers, both in the pose and in the image space. We also show that image jacobian controllers, that use uniform feature depths, prove to be effective alternatives, whenever sensor calibration or depth estimation are inaccurate.