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In order to deal with the scaling problem of volumetric map representationswe propose spatially local methods for high-ratio compression of 3D maps,represented as truncated signed distance fields. We show that these compressedmaps can be used as meaningful descriptors for selective decompression inscenarios relevant to robotic applications. As compression methods, we compareusing PCA-derived low-dimensional bases to non-linear auto-encoder networks andnovel mixed architectures that combine both. Selecting two application-orientedperformance metrics, we evaluate the impact of different compression rates onreconstruction fidelity as well as to the task of map-aided ego-motionestimation. It is demonstrated that lossily compressed distance fields used ascost functions for ego-motion estimation, can outperform their uncompressedcounterparts in challenging scenarios from standard RGB-D data-sets.

An Eigenshapes Approach to Compressed Signed Distance Fields and Their Utility in Robot Mapping