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Truly autonomous operation for any field robot relies on a well-defined pyramid of technical competencies. Consider the case of an autonomous car – we require the vehicle to be able to perceive its environment through noisy sensors, robustly fuse this information into an accurate representation of the world, and use this representation to plan and execute complex tasks – all the while dealing with the uncertainties inherent in real world operation. Of fundamental importance to all these capabilities is localisation – we always need to know where we are, if we are to be able to plan where we are going (or how to get there). As road vehicles make the push towards becoming truly autonomous, the system’s ability to stay accurately localised over its operating lifetime is of crucial importance – this is the core issue of lifelong localisation. The goals in this thesis are threefold – to develop the hardware needed to reliably acquire data over large scales, to build a localisation framework that is robust enough to be used over the long–term, and to establish a method of adapting our framework when necessary such that we can accommodate the inevitable difficulties present when operating over city-scales. We begin by developing the physical means to make large-scale localisation achievable, and affordable. This takes the form of a stand-alone, rugged sensor payload – incorporating a number of sensing modalities – that can be deployed in either a mapping or localisation role. We then present a new technique for localisation in a prior map using an information theoretic framework. The core idea is to build a dense retrospective sensor history, which is then matched statistically within a prior map. The underlying idea is to leverage the persistent structure in the environment, and we show that by doing so, it is possible to stay localised over the course of many months and kilometres. The developed system relies on orthogonally-oriented ranging sensors, to infer both velocity and pose. However, operating in a complex, dynamic, setting (like a town centre) can often induce velocity errors, distorting our sensor history and resulting in localisation failure. The insight into dealing with this failure is to learn from sensor context – we learn a place-dependent sensor model and show that doing so is vital to prevent such failures. The integration of these three competencies gives us the means to make inex- pensive, lifelong localisation an achievable goal.