Evaluation of SLAM based mobile laser scanning and terrestrial laser scanning in the Kiruna mine : A comparison between the Emesent Hovermap HF1 mobile laser scanner and the Faro Laser Scanner Focus3D X 330 terrestrial laser scanner

Abstract: The mining industry has over the last few decades seen a drastic increase in the usage of laser scanning technologies as a way of creating 3D maps of the mines being exploited. Underground mapping in places such as mines has become more prevalent as the technology has progressed and made it easier to generate highly detailed point clouds faster. A newer and faster method of generating point clouds is using a simultaneous localization and mapping (SLAM) based mobile laser scanner (MLS). With the help of complex algorithms, it enables instant point cloud registration and allows for continuous mapping of the surrounding environment while tracking the device location without needing a connection to GPS. As the accuracy and speed of SLAM based MLS continues to improve, its use is becoming far more widespread within the mining industry. Although studies have been conducted previously investigating the differences in quality between SLAM based MLS and terrestrial laser scanners (TLS), there is still a need for further studies conducted in mining environments. This case study aims to investigate the quality differences between two point clouds generated using an Emesent Hovermap HF1, which is a SLAM based MLS, and a Faro Laser Scanner Focus 3D X 330 TLS. Parameters like root mean square (RMS) were investigated. Volume calculations were carried out for both point clouds and compared to each other as well the calculated volume of a theoretical model. To conduct this study data from LKAB’s Kiruna mine was collected and provided by Blå Projekt, Process & GIS AB. The result of this study concludes that the Faro TLS is superior in terms of point cloud quality, with five times better RMS values and higher point density than the Hovermap MLS. It also shows that both scanners allowed for accurate volume calculations with only roughly 1% difference in the estimated volumes. The TLS method yielded a much more readable point cloud with clearer visual details than the SLAM based MLS method. This may however be a result of SLAM drift since no loop closure was performed when collecting the MLS data which otherwise could’ve minimized the errors. It was concluded that due to the amount of data processing required and the longer work time of TLS, SLAM based MLS is a method that is worth further development as it provides unparalleled flexibility, safety improvements and work time efficiency.