Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity

TL;DR

This paper introduces EA-NDT, a semantic segmentation-based framework that enhances HD map compression and descriptivity for autonomous vehicle navigation, outperforming standard NDT in efficiency and detail.

Contribution

The novel EA-NDT framework leverages semantic-aided clustering to improve map compression and descriptivity, advancing HD map representation for autonomous vehicles.

Findings

EA-NDT achieves at least 1.5x higher compression than standard NDT.

EA-NDT maintains the same descriptive capability with fewer cells.

EA-NDT produces maps with higher descriptivity scores.

Abstract

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.