In this paper the performance of a topological- metric visual path following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3D geometries are reconstructed between the neighboring reference images in order to achieve fast feature prediction which allows the recovery from tracking failures. During navigation the robot is controlled using image-based visual servoing. The experiments show that the framework is robust against moving objects and moderate illumination changes. It is also shown that the system is capable of on-line path learning.