  High-Definition (HD) maps are needed for robust navigation of autonomous
vehicles, limited by the on-board storage capacity. To solve this, we propose a
novel framework, Environment-Aware Normal Distributions Transform (EA-NDT),
that significantly improves compression of standard NDT map representation. The
compressed representation of EA-NDT is based on semantic-aided clustering of
point clouds resulting in more optimal cells compared to grid cells of standard
NDT. To evaluate EA-NDT, we present an open-source implementation that extracts
planar and cylindrical primitive features from a point cloud and further
divides them into smaller cells to represent the data as an EA-NDT HD map. We
collected an open suburban environment dataset and evaluated EA-NDT HD map
representation against the standard NDT representation. Compared to the
standard NDT, EA-NDT achieved consistently at least 1.5x higher map compression
while maintaining the same descriptive capability. Moreover, we showed that
EA-NDT is capable of producing maps with significantly higher descriptivity
score when using the same number of cells than the standard NDT.
