Résumé : This paper presents a monocular vision framework enabling feature-oriented appearance-based navigation in large outdoor environ- ments containing other moving ob jects. The framework is based on a hybrid topological-geometrical environment representation, constructed from a learning sequence acquired during a robot motion under hu- man control. The framework achieves the desired navigation functional- ity without requiring a global geometrical consistency of the underlying environment representation. The main advantages with respect to con- ventional alternatives are unlimited scalability, real-time mapping and effortless dealing with interconnected environments once the loops have been properly detected. The framework has been validated in demanding, cluttered and interconnected environments, under different imaging con- ditions. The experiments have been performed on many long sequences acquired from moving cars, as well as in real-time large-scale navigation trials relying exclusively on a single perspective camera. The obtained results imply that a globally consistent geometric environment model is not mandatory for successful vision-based outdoor navigation.