  Hierarchical topological representations can significantly reduce search
times within mapping and localization algorithms. Although recent research has
shown the potential for such approaches, limited consideration has been given
to the suitability and comparative performance of different global feature
representations within this context. In this work, we evaluate state-of-the-art
hand-crafted and learned global descriptors using a hierarchical topological
mapping technique on benchmark datasets and present results of a comprehensive
evaluation of the impact of the global descriptor used. Although learned
descriptors have been incorporated into place recognition methods to improve
retrieval accuracy and enhance overall recall, the problem of scalability and
efficiency when applied to longer trajectories has not been adequately
addressed in a majority of research studies. Based on our empirical analysis of
multiple runs, we identify that continuity and distinctiveness are crucial
characteristics for an optimal global descriptor that enable efficient and
scalable hierarchical mapping, and present a methodology for quantifying and
contrasting these characteristics across different global descriptors. Our
study demonstrates that the use of global descriptors based on an unsupervised
learned Variational Autoencoder (VAE) excels in these characteristics and
achieves significantly lower runtime. It runs on a consumer grade desktop, up
to 2.3x faster than the second best global descriptor, NetVLAD, and up to 9.5x
faster than the hand-crafted descriptor, PHOG, on the longest track evaluated
(St Lucia, 17.6 km), without sacrificing overall recall performance.
