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.
Abstract (translated)
同时定位与映射（SLAM），即通过（3D）地图重建环境，同时进行姿态估计，已经取得了令人惊讶的进展。与此同时，旨在在复杂环境中进行数据收集的大规模应用变得更加可行。然而，与单间建筑内部的小规模场景相比，车间或建筑区需要在大规模距离内测量在可能纹理不足的区域上的措施。由于通常没有GNSS测量方法，因此对于这种室内应用，姿态估计进一步加剧。在我们的工作中，我们通过配备四个立体摄像头和3D激光扫描仪的机器人系统在大型工厂车间中实现了数据收集。我们应用了最先进的LiDAR和视觉SLAM方法，并讨论了不同传感器类型对于轨迹估计和密集地图生成的优缺点。此外，通过3D高斯扩展生成密集且准确的深度图，我们计划在致力于自动建筑和场地监测的项目中使用。
URL
https://arxiv.org/abs/2404.17215
