Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks

TL;DR

This paper introduces an efficient, scalable LiDAR reconstruction system capable of real-time large-scale 3D mapping up to 60 meters range, suitable for robotic exploration in diverse environments.

Contribution

It extends existing RGB-D reconstruction methods to support long-range LiDAR data, enabling dynamic correction and scalability for large-scale exploration tasks.

Findings

Reconstructs at 3 Hz with 60 m range and ~5 cm resolution.

Outperforms state-of-the-art methods in range and resolution.

Supports real-time large-scale exploration in diverse environments.

Abstract

We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called supereight, to support LiDAR scans and a newly developed submapping technique to allow for dynamic correction of the 3D reconstruction. We then introduce a novel pose graph clustering and submap fusion feature to make the proposed system more scalable for large environments. We evaluate the performance using two public datasets including outdoor exploration with a handheld device and a drone, and with a mobile robot exploring an underground room network. Experimental results demonstrate that our system can reconstruct at 3 Hz with 60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can only reconstruct to 25 cm resolution or 20 m range at the same frequency.