SLIM: Scalable and Lightweight LiDAR Mapping in Urban Environments

TL;DR

SLIM is a novel LiDAR mapping system that creates scalable, lightweight, and accurate maps for urban environments, enabling long-term robot navigation with low memory usage.

Contribution

Introduces SLIM, a new mapping approach that uses structural representations and map-centric optimization for scalable, lightweight, and accurate long-term LiDAR mapping.

Findings

Achieves high mapping accuracy with low memory (~130 KB/km).

Demonstrates effective map re-use for robot localization.

Validates scalability and consistency across multiple datasets.

Abstract

LiDAR point cloud maps are extensively utilized on roads for robot navigation due to their high consistency. However, dense point clouds face challenges of high memory consumption and reduced maintainability for long-term operations. In this study, we introduce SLIM, a scalable and lightweight mapping system for long-term LiDAR mapping in urban environments. The system begins by parameterizing structural point clouds into lines and planes. These lightweight and structural representations meet the requirements of map merging, pose graph optimization, and bundle adjustment, ensuring incremental management and local consistency. For long-term operations, a map-centric nonlinear factor recovery method is designed to sparsify poses while preserving mapping accuracy. We validate the SLIM system with multi-session real-world LiDAR data from classical LiDAR mapping datasets, including KITTI, NCLT, HeLiPR and M2DGR. The experiments demonstrate its capabilities in mapping accuracy, lightweightness, and scalability. Map re-use is also verified through map-based robot localization. Finally, with multi-session LiDAR data, the SLIM system provides a globally consistent map with low memory consumption (~130 KB/km on KITTI).