Simultaneous Localization and Mapping is a fundamental problem for robots interacting with a novel environment and has been a densely studied area of research for several decades. The modern paradigm of feature extraction and matching coupled with advancements in sensor technology have allowed robots to achieve sub meter localization accuracy over kilometer long trajectories in controlled indoor and urban environments. Despite these advancements, as roboticists endeavour to deploy agents in more unstructured outdoor settings to perform search and rescue or geological survey, the standard assumptions adopted by the majority of the community start to break down. In this talk we will discuss the SLAM paradigm at a high level, how these assumptions break down in the outdoor-unstructured setting and existing strategies for mitigation. We will present work evaluating state of the art SLAM algorithms in the geology context, develop SLAM in an underwater ocean setting under extreme sensing constraints, and extend this framework for active SLAM in this context.