MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection

TL;DR

This paper introduces MS-Mapping, a multi-session LiDAR mapping system that uses Wasserstein distance for real-time keyframe selection, reducing data redundancy and improving efficiency in large-scale environments.

Contribution

It proposes a novel Wasserstein-based keyframe selection method and an incremental mapping scheme for efficient large-scale LiDAR mapping.

Findings

Reduces data redundancy in multi-session LiDAR mapping

Improves graph optimization efficiency with Wasserstein-based keyframe selection

Provides publicly available code and datasets for further research

Abstract

Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available.