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Topological localization is a qualitative solution approach that can assist obtaining a faster quantitative metric solution by limiting the searchable space. Consequently, its efficiency is an essential requirement in hierarchical localization frameworks. This paper presents a topological map generation method with a localization scheme. Good compromise of performance measures - accuracy, memory and processing time - indicates the method's efficiency. The suggested implementations rely on information-theoretic selection of local features for node distinctive representation, and a visual codebook for compression. Testing the proposed approach on the COLD database, a recent specific benchmarking database for robotic topological mapping and localization, reveals its customization according to the vision sensor and environment characteristics. The approach guarantees over 90% localization accuracy with more than 50% overhead reduction, and is suitable for application in highly unstructured cluttered environments that are influenced by dynamics and illumination variations.