*4.3.2 Global registration*

To minimize the errors of pairwise registration of many point clouds, all point clouds are registered globally. Again, the algorithms are easily interchangeable. Mostly currently the GraphSLAM [24] algorithm is used after Lu and Milios [25]. It creates a graph of the connections between all point clouds with overlaps and minimizes the alignment errors of all connections simultaneously.

#### *4.3.3 Conversion to 3D map*

As a representation of the 3D map, an OctoMap [26] is used. This offers the advantage of being able to query the information in various resolutions and to map the distinctions that are important for navigation between free, occupied and unknown cells.

**Figure 4** represents the result from the 3D mapping process from an exploratory trip in the underground mine from maxit in Krölpa, Germany. The mobile robot scans every 10 m a full-sphere point cloud. The point cloud data and the odometry data are saved into a rosbag file and are processed with the mapit workflow. **Figure 4a** shows a 2D occupancy grid of the mapped part of the mine. **Figure 4c** visualizes the point clouds themselves and **Figure 4b** a 3D OctoMap from the point clouds.

from the stop-and-go FARO scan point cloud data in gray values and the live sensor data over 2 seconds from the SWAP platform in color values. **Figure 6b** shows in a top-down view the real-time data from one Velodyne Puck scanner with 20 Hz in

The exploration vehicle can scan automatically with the automation adapter of the FARO Focus laser scanner. A main disadvantage of this automation adapter is that all drivers for the FARO scanner support only the Windows operating system. To overcome this problem, several Windows applications are developed and can be selected via ROS. Firstly, the scanning application sends user-selected scan parameter over ROS to the scanner and starts the scanning process. The scanned data are stored in the customer format on the hard disk. The second application converts the customer format of a point cloud into the free PCD format of the Point Cloud Library (PCL) and stores the new format again. Last but not least, the PCD data files are loaded and

are visualized with RViz in ROS. As an option, the PCD data can be filtered.

be calculated more efficiently using the octree data structure.

*Visualization from the mapit GUI to render 13 registered point clouds.*

*A System for Continuous Underground Site Mapping and Exploration*

*DOI: http://dx.doi.org/10.5772/intechopen.85859*

The huge amount of point cloud data can be decomposed with an octree space partitioning algorithm (see **Figure 7**). This data structure is suitable for local collision detection, downsampling the huge data amount and representing the data in different resolutions. Especially, the normal estimation uses a neighboring search to every point of the point cloud. This step and the viewer-dependent resolution can

*Live sensor data from the SWAP platform of the exploration vehicle at the maxit mine, Krölpa, Germany. (a) Grey values: Stop-and-go data from FARO Focus3D X 130. Color values: Live data from SWAP platform over*

*2s and (b) Top down perspective from Velodyne VLP-16 puck data, 20Hz in blue and reds.*

blue and red color values.

**Figure 5.**

**Figure 6.**

**73**
