My thesis considered the problem of scaling robotic mapping and localization to larger unknown environments. I studied feature-based algorithms for simultaneous localization and mapping (SLAM) whereby a robot builds a map of the world while concurrently estimating its position in the map. My thesis work proposed a sparse information filter algorithm that is scalable and also preserves estimate consistency. The approach maintains a Gaussian probability distribution over the robot and map states, and takes advantage of insights into the natural structure of this model for SLAM. The Exactly Sparse Extended Information Filter (ESEIF) exploits a sparse parametrization of this distribution to reduce the computational and memory costs from quadratic to linear in the map's size. In addition to the gains in efficiency, a primary contribution of the algorithm is its ability to achieve sparsity in a principled, yet simple way that preserves consistency.