  A robust, resource-efficient, distributed, and minimally parameterized 3D map
matching and merging algorithm is proposed. The suggested algorithm utilizes
tomographic features from 2D projections of horizontal cross-sections of
gravity-aligned local maps, and matches these projection slices at all possible
height differences, enabling the estimation of four degrees of freedom in an
efficient and parallelizable manner. The advocated algorithm improves
state-of-the-art feature extraction and registration pipelines by an order of
magnitude in memory use and execution time. Experimental studies are offered to
investigate the efficiency of this 3D map merging scheme.
