*4.3.1 Pairwise registration*

**4.2 Workflow with mapit**

*Unmanned Robotic Systems and Applications*

requirements.

**Exploratory trip**

integrated into the mapit system.

are then converted to a 3D occupancy map.

**3D to 2D map conversion/transformation**

**Registration and mapping**

**Subsequent exploration**

additional reconnaissance.

**4.3 3D mine mapping with mapit**

integrating the robot's movements.

the modified transformations.

**70**

the previous one.

**Map extension**

This subsection describes the workflow with mapit to develop a 3D map from a test drive of the exploration vehicle. Mapit includes various requirements to support the work of 3D map creation. Firstly, it must have open and flexible interfaces to import and export sensor data and maps, as well as being easily adaptable to new requirements. Secondly, the data processing must be simple, reproducible and, in particular, traceable. Thirdly, it must have open and flexible interfaces to import and export sensor data and maps, as well as being easily adaptable to new

**Figure 3b** represents a mapit workflow from the exploration vehicle and the

During the exploratory trip, every 10 full-sphere point clouds is recorded. These point clouds are stored together with the odometry data via the Robot Operating System (ROS) software with rosbag. After the exploratory trip, this data will be

In mapit, these point clouds are aligned to a consistent map using various registration algorithms (operators) and multiple passes. These aligned point clouds

are made in the mine's 3D map, and a 2D occupancy map is created.

The exploration vehicle and the production vehicle are located via 2D scanners, which measure the surface of the mine in one section. For these two vehicles, cuts

During the subsequent exploration, the exploration vehicle locates itself in this created 2D map or drives outside of it. New full-sphere point clouds of known and unknown areas are recorded and integrated into the mapit system after the trip to

Then, similar to the point *registration and mapping*, new point clouds are registered to the already aligned point clouds and the new extended map is then created

To compute a consistent map based on the data collected by the mobile platform, we use spherical point clouds and minimize the error in merging them by

In order to integrate all sensor data to one global map, the algorithms in mapit do not operate on the data but only on the transformations on this data. This is why registration algorithms, for example, can be run on different resolutions or on different data without the data being modified: the results just change according to

with all data from previous reconnaissance trips and the new data from the

process vehicle. The figure has the following steps:

The point clouds recorded during the exploratory trip are first registered in pairs. For this, the iterative closest point (ICP) method [22] is used, which is an iterative minimum method. There are always two consecutive point clouds compared. It searches, from each point of the one point cloud, the next point in the other point cloud and minimizes this between all pairs of points minimized. The initials of the algorithm are the odometry provided by the AMT. Therefore, a general implementation has been created so that different algorithms, for example, can be easily integrated from the Point Cloud Library (PCL) [23]. To create the maps, the algorithm iterative closest point (ICP) has been used, which forms pairs of points between two point clouds and minimizes the distance between these pairs. The initial values for these algorithms are primarily the odometry provided by the RWTH Aachen University Institute for Advanced Mining Technologies (AMT). This results in a good orientation of the point clouds but typically remains a small

error. This becomes visible after long scan series (e.g. in the map of maxit, Krölpa, of approximately 800 m range).
