As the field of sensor networks matures, research in this area is focusing not only on fixed networks, but also on mobile sensor networks. For many reasons, both technical and logistical, such networks will often be very sparse for all or part of their operation, sometimes functioning more as disruption-tolerant networks (DTNs). While much work has been done on localization methods for densely populated fixed networks, most of these methods are inefficient or ineffective for sparse mobile networks, where connections can be infrequent. While some mobile networks rely on fixed location beacons or per-node, onboard GPS, these methods are not always possible due to cost, power and other constraints. In this paper we present the Low-density Collaborative Ad-Hoc Localization Estimation (LOCALE) system for sparse sensor networks. In LOCALE, each node estimates its own position, and collaboratively refines that location estimate by updating its prediction based on neighbors it encounters. Nodes also estimate (as a probability density function) the likelihood their prediction is accurate. We evaluate LOCALE'S collaborative localization both through real implementations running on sensor nodes, as well as through simulations of larger systems. We consider scenarios of varying density (down to 0.02 neighbors per communication attempt), as well as scenarios that demonstrate LOCALE'S resilience in the face of extremely-inaccurate individual nodes. Overall, our algorithms yield up to a median of 21X better accuracy for location estimation compared to existing approaches. In addition, by allowing nodes to refine location estimates collaboratively, LOCALE also reduces the need for fixed location beacons (i.e. GPS-enabled beacon towers) by as much as 64X.