For the foreseeable future, robots operating beyond Earth will have to rely on triangulating rover position on a map or tracking the sun or stars. These approaches have shortcomings including limited resolution of orbital data and required interaction with ground control. SLAM is a promising means of infrastructure-free localization using local information; but unfortunately, most state-of-the-art SLAM implementations are not yet suitable for planetary exploration. Their implementations depend upon easily-recognizable landmarks that planetary environments lack. SLAM's computational complexity grows quickly with map size making it difficult to maintain kilometer-scale maps, especially on space-relevant computing hardware. MeshSLAM is significant to NASA because it provides planetary-relevant rover localization and mapping without orbital information, ground communication, or excessive computation. Furthermore in barren terrain its results will be more accurate than current methods. The partnership between Carnegie Mellon and Mesh Robotics is committed to developing and maintaining MeshSLAM following an open-source philosophy. Our aim is to leverage our years of experience working with NASA research groups to mature and prepare MeshSLAM for missions of the future. MeshSLAM will add value to long-duration missions involving repeated travel, such as manned-mission pre-cursors, site preparation, and long-range mapping. Even on Earth, accurate localization remains a challenge in frequent situations where GPS is unavailable, either temporarily (e.g., passing under bridges or operating near buildings) or permanently (e.g., indoors and underground, or when GPS is jammed). As a result, the mining, agriculture, defense, and automotive industries are investing heavily in localization technologies. Companies (e.g., Applanix, NovAtel) have seen healthy growth in the past decade by providing off-the-shelf inertial navigation systems (INSs) that fuse GPS readings with data from inertial measurement units. Unfortunately, the underlying drift of even high-quality inertial measurements is severe and thus, localization estimates diverge dramatically within minutes of a loss of GPS. MeshSLAM can complement these existing techniques and improve their accuracy in GPS-denied situations. In unmanned-vehicle applications, MeshSLAM uses data from sensors already integrated for perception, so no new equipment is required. Furthermore, MeshSLAM's efficiency makes it suitable for running on highly-integrated embedded platforms.