MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework

Abstract

Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) still remains challenging due to three limitations: lack of unified standards, poor robustness to noise, and computational inefficiency. We propose MapEval, a novel framework for point cloud map assessment. Our key innovation is a voxelized Gaussian approximation method that enables efficient Wasserstein distance computation while maintaining physical meaning. This leads to two complementary metrics: Voxelized Average Wasserstein Distance (AWD) for global geometry and Spatial Consistency Score (SCS) for local consistency. Extensive experiments demonstrate that MapEval achieves $100$- $500$ times speedup while maintaining evaluation performance compared to traditional metrics like Chamfer Distance (CD) and Mean Map Entropy (MME). Our framework shows robust performance across both simulated and real-world datasets with million-scale point clouds.