*A System for Continuous Underground Site Mapping and Exploration DOI: http://dx.doi.org/10.5772/intechopen.85859*

platform consists of a Velodyne VLP-16 PUCK LiDAR and a Hokuyo UTM-30LX-EW range scanner which are both mounted opposite to each other on a disk which rotates both scanning devices around the centre of the disk. The disk and the upper part of the scanner are driven by a motor which is equipped with absolute encoders. Both scanners transfer their data via Ethernet which is connected by a slip ring which connects the revolving part to the rest of the scanning device. The combination of motor and gear head provides us with 3 Nm of torque and allows for a maximum rotation speed of 2.6 Hz. However, a reasonable azimuth resolution can only be achieved with a scanning speed of up to 1.67 Hz, while the full-sphere point clouds are then captured with a half revolution which equals 3.34 Hz for this. We deploy a 14 bit industrial grade absolute SSI encoder which is mounted on the drive shaft. The resolution provides a maximum error of 1.32<sup>0</sup> or 0.022°. In a distance of 10, this corresponds to 3.8. The second part of the platform is the rotating sensor mount. It houses a gigabit Ethernet switch, the interface box of the Velodyne VLP-16 PUCK and the Hokuyo UTM-30LX-EW, the power distribution for the sensors and several mounting rails for different sensors. The raw data of the deployed Velodyne VLP-16 PUCK and the attached Hokuyo UTM-30LX-EW are registered making use of the SSI absolute encoder. Besides the absolute encoders, there is another incremental encoder attached to the motor shaft. Then, based on the readings of the absolute encoder, the raw data is collected and integrated into a point cloud for the device. This is done with a best-effort time-stamping on the data and where one UDP package of the Velodyne VLP-16 PUCK is transformed altogether. The time difference between the laser readings within one UDP package is about 1.33 ms. For the rectification of the Hokuyo UTM-30LX-EW measurements, the recording time for one sweep is taken into account. As a final ingredient, our SWAP platform is equipped with an IMU (*μ*IMU from NG1 ) for providing the orientation of the platform w.r.t. the ground. **Figure 2** shows a CAD drawing as well as a photo of the device.

#### **Figure 2.**

The mapping operation is not run autonomously in the mine environment for now. At the front of the robot, an Allied Vision GT6600C high-resolution camera with a wide-angle lens is mounted. The camera can be used for teleoperation.

*Exploration robot developed for mapping underground mining sites. (a) Exploration vehicle and (b) Sensor*

Measuring range 0.1–100 m 0.6–130 Horizontal resolution 2° 0.035° Vertical resolution 0.4° 0.07° Sphere coverage 80.27% 83.33% Scan time 0.3–30 s 1–30 min

**SWAP platform FARO Focus3D X 130**

As an additional safety feature, we mounted a FLIR A315 thermal camera at the front of the robot in order to be able to detect persons even when not sufficient light

For mapping the mine, the platform is equipped with a rotating 3D LiDAR system, the SWAP platform, which we will describe in detail in the next section. For reference, we mounted a FARO Focus3D X 130 LiDAR, which can be used in a stopand-go fashion. Scanning times of the Focus LiDAR lie between 1 and 30 min. To remotely operate the LiDAR, we developed a ROS driver based on the FARO SDK. As part of our project contribution, we developed a rotating sensor platform for the swift acquisition of dense point clouds as reported in [16]. The main goal was to find a compromise between acquiring accurate and dense point clouds which usually takes much time and having available data for online use in a robotic system for tasks such as localization which has to be updated more frequently. For instance, taking the FARO LiDAR with an angular resolution of 0.0035°, very dense and accurate point clouds can be recorded. However, the robot needs to stand still, and the scanning time of a single scan can take up to 30 min. **Table 1** shows a compar-

In this section, following previous work in [16], we present the 3D LiDAR platform SWAP which was developed during the mine mapping project. The SWAP

is available.

**Table 1.**

**Figure 1.**

*setup of the exploration robot.*

*Unmanned Robotic Systems and Applications*

ison of the two scanners.

**66**

**3. The 3D LiDAR system SWAP**

*Comparison between SWAP and FARO Focus3D X 130.*

*The components and a photo of our rotating sensor platform. (a) Components of the platform and (b) Photo of the platform*

<sup>1</sup> http://www.northropgrumman.litef.com/en/products-services/industrial-applications/product-ove rview/mems-imu/.

The current design was chosen keeping in mind the lessons learnt from its predecessor device which was based on a Velodyne HDL-64. That device was tilted along its vertical axis. It was presented in [18]. While the device is very suitable for acquiring 3D dense point clouds (in [7] we used this device for mapping large-scale motorway tunnels), it has some drawbacks when it comes to the distribution of the range measurement in the point cloud. With a tilting scanner, the point clouds are particularly dense at the turning points of the device and less dense in between.

**Connection**

CloudCompare<sup>3</sup>

logged.

later point.

A network interface has been developed that allows transparent access between

While ROS is used to programme mobile robots and can save all sensor data from a robot test drive, the framework mapit was developed to manage and save the post-processing of all sensor data. There are three basic principles similar to a

1. Data and the executed algorithms are stored together: At any time, the origin of a map can be traced—the basic sensor data, the used algorithms and

2. Every access to the data must be done by mapit: This concept is like a version control system. The development and history of the post-processing are

3. Algorithms are deterministic (if possible): Data can be deleted and restored at a

All map data and algorithms can be stored like in a directory system. **Figure 3a**

*mapit concept and workflow. (a) Visualization of the mapit concept and (b) Workflow in mapit with the*

shows the structure of a management process, i.e. to develop a map. The repository corresponds to a project. Each project arranges its data in trees and entities, i.e. the point clouds. Each workspace consists of the post-processing algorithm workflow, i.e. to develop a map from registered point clouds.

) has been implemented to work efficiently through plugins.

local and remote mapit instances. A connection to external software (e.g.

version control system that have been implemented in mapit:

*A System for Continuous Underground Site Mapping and Exploration*

parameter for the post-processing.

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

<sup>3</sup> https://github.com/MASKOR/cc\_qMapit\_IO\_plugin.

*exploration vehicle and the processing vehicle involved.*

**Figure 3.**

**69**

For the SWAP design, we therefore changed the setup from a tilting 3D LiDAR to a rotating device. With the additional tilt angle in the mounting position of the VLP-16, we achieve an even distribution of points in the scanning range of the device. The additional Hokuyo rangefinder was mounted in order to acquire sensor data in a close range around the robot, as the measurement range of the VLP-16 starts at about 0.9 m.

Analyzing and optimizing the homogeneity of a scan are investigated in [19]. For our target scenarios, the sensor platform yields an optimized compromise between the scan acquisition speed and the point cloud density. By adjusting the rotation frequency, the map resolution and the time needed to record the map can also be balanced.
