**3.3 Test in real scenario**

Once the algorithm and its procedure were validated in all virtual scenarios, the behaviour of the system was tested in a real environment.

The first test carried out using the drone in real scenario was operated in a facility with technical characteristics described in **Table 2** and **Figure 10**.

During the test a precise routine has been followed:

1.UAV positioning at the beginning of the tunnel.

2.System power-on and lipo-battery connection on UAV.

3.Check communication link between UAV and ground station.

4.Execution of ROS launch file.

5.Set up mission parameters.

6.Start mission.

The types of tests that have been performed are divided into two categories:


**Table 3** shows the results obtained for type B condition and the relative absolute error calculated as the difference in Euclidean distance traveled by the UAV between the point of take-off and point of landing. The distances over which the tests were performed are respectively 10, 20 and 30 m, iterated 10 times in order to compare the error related to the odometry data. **Table 4** displays the average minimum and maximum error for each different test.


**159**

**Figure 10.** *View of the tunnel.*

> **Test (m)**

**Table 3.**

**Table 4.**

odometric error during navigation.

*Minimum, maximum and average error.*

*Absolute error in metres for each different test lengths.*

*Visual-Inertial Indoor Navigation Systems and Algorithms for UAV Inspection Vehicles*

The minimum error obtained for the various ranges of distance tested is consistent with the results obtained in other recent works of the literature [13, 14]. At the same time, if we analyse the average error obtained by performing multiple consecutive tests for each range of distance, it can be seen that an improvement in the visual-inertial system is possible, although the system already guarantees great robustness in operation. An improvement could be obtained by using a different hardware, more performing IMU, and at the same time deepening the aspect related to the calibration of the camera in order to further succeed in decreasing the

Minimum 0.280 0.335 0.113 Maximum 1.269 1.369 1.572 Average 0.794 0.850 0.755

**1° 2° 3° 4° 5° 6° 7° 8° 9° 10°**

**10 m 20 m 30 m**

10 m **0.701** 0.867 0.383 1.197 0.280 0.981 0.840 1.111 1.269 0.311 20 m **1.369** 1.150 0.511 0.731 0.403 0.732 1.208 0.820 1.242 0.335 30 m **1.35** 0.610 0.113 0.689 0.223 0.134 1.383 1.572 1.175 0.301

After conducting a series of test in facility, we definitively validate the result of the project and the system design in a different real tunnel (**Figure 11**). In this situation it was confirmed that the precision and reliability of the algorithms were enough to allow the system to navigate in total autonomy for at least a stretch of 100 metres.

*DOI: http://dx.doi.org/10.5772/intechopen.90315*

**Table 2.** *Characteristics of the tunnel.* *Visual-Inertial Indoor Navigation Systems and Algorithms for UAV Inspection Vehicles DOI: http://dx.doi.org/10.5772/intechopen.90315*
