**Connection**

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. 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

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

There exist a number of toolkits that help with registering point clouds and process 3D data. Many of them are professional software products, often also provided from the manufacturer of a 3D LiDAR system. Also a number of Open Source projects exist, for instance, the 3D Toolkit (3DTK) [20, 21]. The 3DTK provides a number of state-of-the-art 6D SLAM algorithm for registering point cloud data as well as a large number of additional shape detection and visualization

In contrast to this, mapit focuses more strongly on the registration workflow and the post-processing of map data, but it is not restricted only to point cloud data. In mapit, additional sensor cues could be associated with the 3D data. The key idea of mapit is to store the raw data and keep track of all algorithm steps over time.

The sensor data are loaded and stored persistently in a database. The access is via defined interfaces. All changes to the data are stored individually. As a result, work

The algorithms for filtering the sensor data, the registration tree of the 3D point

clouds, the creation of the 3D map and the further processing of the map are

With mapit, we developed a 3D mapping framework<sup>2</sup> for managing and post-processing a wide range of sensor data, especially the point clouds from the exploration vehicle. The software is divided into components and is designed for

extensibility. We describe the different fields below:

steps and results are stored together consistently.

starts at about 0.9 m.

also be balanced.

tools.

**4.1 Overview**

**Management/administration**

defined and developed with mapit.

<sup>2</sup> https://github.com/MASKOR/mapit.

**Algorithm processing**

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**4. The map registration system mapit**

*Unmanned Robotic Systems and Applications*

A network interface has been developed that allows transparent access between local and remote mapit instances. A connection to external software (e.g. CloudCompare<sup>3</sup> ) has been implemented to work efficiently through plugins.

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 version control system that have been implemented in mapit:


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

#### **Figure 3.**

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

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