SLAM for Indoor Mapping of Wide Area Construction Environments

TL;DR

This paper demonstrates the use of advanced SLAM techniques with LiDAR and stereo cameras for large-scale indoor mapping in construction environments, addressing challenges like textureless areas and lack of GNSS signals.

Contribution

It presents a comprehensive approach combining LiDAR and visual SLAM for large-scale indoor mapping, including dense depth map generation with 3D Gaussian splatting.

Findings

LiDAR SLAM provides robust trajectory estimation in textureless environments.

Visual SLAM offers detailed dense mapping but faces challenges in large, feature-sparse areas.

Dense depth maps enable improved site monitoring and automatic construction analysis.

Abstract

Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring.