We build upon our prior work applying subdivision surfaces (SUBDIVS) to planetary terrain mapping. SUBDIVS are an alternative, multi-resolution method with many advantages over conventional digital…

This is a firm fixed-price, Small Business Innovative Research (SBIR) Phase I contract awarded by the National Aeronautics and Space Administration (NASA) to Digitalfish, Inc., a self-certified small disadvantaged business. The contract, valued at $122,243.00, is for the development of a prototype mapping system using subdivision surfaces (subdivs) as a representation for terrain data with varied spatial resolution and 3D features. The research aims to extend Digitalfish's prior work in applying subdivs to planetary terrain mapping, focusing on improvements to subdiv algorithms, techniques, and theory, as well as new methods for merging terrain data from multiple sources. The expected benefits include higher-fidelity terrain visualization, the ability to visualize 3D features missed in traditional digital elevation maps, and greater algorithmic efficiency for non-visualization scientific computation.

We build upon our prior work applying subdivision surfaces (SUBDIVS) to planetary terrain mapping. SUBDIVS are an alternative, multi-resolution method with many advantages over conventional digital elevation maps (DEM's) and fixed-resolution meshes. The proposed research is innovative in presenting a new setting for SUBDIVS demanding novel extensions to subdiv algorithms, techniques and theory as well as new methods in merging of terrain data from multiple sources. Our primary objectives are to: (1) develop a prototype mapping system using SUBDIVS as a representation for terrain data with highly varied spatial resolution and 3-D features; (2) extend our novel volumetric merging method, integrating input data at varied confidence levels from varied source formats (DEM, point cloud, range data, etc.) while supporting overhanging and cave-like terrain geometry; (3) demonstrate collaborative use of registered surface detail with terrain-mapped data fields such as terrain color, confidence estimates, and science-data overlays; and (4) show, via high-quality DEM extraction, compatibility with existing systems including applicability for autonomous processing on small, weight- and power-constrained (SWAP) robots. The expected benefits are: (a) higher-fidelity terrain visualization with reduced processing error and lower infrastructure requirements; (B) ability to visualize 3-D features, such as overhangs, missed in DEM's; (C) compact encoding with natural level-of-detail control for interactive viewing on mobile devices; (D) greater algorithmic efficiency for non-visualization scientific computation; and (E) enablement of new software-tool capabilities for dynamic mapping of alternative local-terrain datasets, non-destructive experimentation, collaboration, and data traceability. The innovation also promises capability and reliability benefits to robots by unifying terrain representations and enabling minimal upload of only incremental terrain details from the ground.