Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis

TL;DR

This paper introduces a scalable, elastic LiDAR reconstruction system that uses spatial analysis to efficiently manage memory and improve accuracy in complex environments, demonstrated through indoor and outdoor experiments.

Contribution

It proposes novel strategies for spawning and fusing submaps based on spatial overlap, enabling scalable reconstruction that adapts to environment size and improves global consistency.

Findings

Enhanced scalability compared to baseline methods

Improved global reconstruction accuracy

Effective segmentation of complex spaces during exploration

Abstract

This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned environment to control memory usage growth by fusing overlapping submaps in different ways. This allows the number of submaps and memory consumption to scale with the size of the environment rather than the duration of exploration. By analysing spatial overlap, our system segments distinct spaces, such as rooms and stairwells on the fly during exploration. Additionally, we present a new mathematical formulation of relative uncertainty between poses to improve the global consistency of the reconstruction. Performance is demonstrated using a multi-floor multi-room indoor experiment, a large-scale outdoor experiment and simulated datasets. Relative to our baseline, the presented approach demonstrates improved scalability and accuracy.