Evaluation of vocabulary trees for localization in robot applications

Vocabulary tree based place recognition is widely used in topological localization and its various applications have been proposed during the past decade. However, the bag-of-words representations from the vocabulary tree, which is trained with fixed training data, are difficult to be optimized to dynamic environments. To solve this problem, an adaptive vocabulary tree has been proposed, but there has been no comparison considering the adaptive properties of the conventional vocabulary tree. This paper provides a performance evaluation of the vocabulary tree and the adaptive vocabulary tree in dynamic scenes. This analysis provides guidance for choosing appropriate vocabulary in robot applications.