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.

翻译：同时定位与建图（SLAM），即通过（三维）地图重建环境并同时估计自身位姿的技术，已取得令人瞩目的进展。与此同时，针对工厂车间或建筑工地等复杂环境的大规模数据采集应用正逐步成为现实。然而，与建筑内部分隔为独立房间的小规模场景不同，车间或施工区域需要在潜在无纹理区域及照明困难的条件下进行远距离测量。由于此类室内应用无法获取GNSS信号，位姿估计的难度进一步加剧。在本研究中，我们通过配备四台立体相机及一台三维激光扫描仪的机器人系统，在大型工厂车间内实现了数据采集。我们采用最先进的LiDAR与视觉SLAM方法，并深入探讨了不同传感器类型在此类环境中进行轨迹估计与稠密地图生成时的优势与局限性。此外，我们利用三维高斯泼溅技术生成高密度精度深度图，该技术将用于本项目中旨在实现自动化施工与场地监测的相关研究。