SLAM for Indoor Mapping of Wide Area Construction Environments
This work addresses the problem of indoor mapping for construction and factory environments, but it is incremental as it applies existing SLAM methods to a new dataset without introducing major innovations.
The paper tackled the challenge of applying SLAM in large, complex indoor construction environments where traditional methods struggle due to long distances, textureless areas, poor lighting, and lack of GNSS signals, by using a robot with stereo cameras and a 3D laser scanner to collect data and compare LiDAR and visual SLAM approaches, resulting in the generation of dense and accurate depth maps via 3D Gaussian splatting for potential use in automatic construction monitoring.
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.