A Sparse Hierarchical Map Representation for Mars Science Laboratory Science Operations

Abstract:

We describe a solution for multi-scale Mars terrain modeling and mapping with Digital Elevation Models (DEMs) and co-registered orthogonally projected imagery (ortho-images). High resolution DEMs and ortho-images derived from Mars Science Laboratory (MSL) rover science and navigation cameras are represented in context with lower resolution, wide coverage DEMs and ortho-images derived from Mars Reconnaissance Orbiter (MRO) HiRISE and CTX camera images and Mars Express (MEX) mission HRSC images. Merging MSL rover image derived terrain models with those from orbital images at a uniform high resolution would require super-sampling of the orbital data across a large area to maintain significant context. This solution is not practical, and would result in a mapping product of enormous size. Instead, we choose a sparse hierarchical map representation. Each level in this hierarchical representation is a map described by a set of tiles with fixed number of samples and fixed resolution. The number of samples in a tile is fixed for all levels and each level is associated with a specific resolution. In this work, the resolution ratio between two adjacent levels is set to two. The map at each level is sparse and it contains only the tiles for which data is available at the resolution of the given level. For example, at the highest resolution level only MSL science camera models are available and only a small set of tiles are generated in a sparse map. At the lowest resolution, the map contains the complete set of tiles. The reference level of the representation is chosen to be the HiRISE terrain model and CTX, HRSC and MSL data are projected onto this model before being mapped. While our terrain representation was developed for use in “Antares”, a visual planning and sequencing tool for MSL science cameras developed at NASA Ames Research Center, it is general purpose and has a number of potential geo-science visualization applications.