Spatiotemporal objects – that is, objects that evolve over time – appear in many applications. Due to the nature of such applications, storing the evolution of objects through time in order to answer historical queries (queries that refer to past states of the evolution) requires a very large specialized database, what is termed in this article a spatiotemporal archive. Efficient processing of historical queries on spatiotemporal archives requires equally sophisticated indexing schemes. Typical spatiotemporal indexing techniques represent the objects using minimum bounding regions (MBR) extended with a temporal dimension, which are then indexed using traditional multidimensional index structures. However, rough MBR approximations introduce excessive overlap between index nodes, which deteriorates query performance. This article introduces a robust indexing scheme for answering spatiotemporal queries more efficiently. A number of algorithms and heuristics are elaborated that can be used to preprocess a spatiotemporal archive in order to produce finer object approximations, which, in combination with a multiversion index structure, will greatly improve query performance in comparison to the straightforward approaches. The proposed techniques introduce a query efficiency vs. space tradeoff that can help tune a structure according to available resources. Empirical observations for estimating the necessary amount of additional storage space required for improving query performance by a given factor are also provided. Moreover, heuristics for applying the proposed ideas in an online setting are discussed. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.