## 4 Discussion and Conclusions  
The way in which firefighters move across buildings during search and rescue operations differs from the common way of movement. Therefore, instead of adopting one of the existing solutions, we decided to address the problem of navigation for firefighters from scratch. In the selection of the approach for error correction, we considered the limitations imposed by our INS as well as the specific environment in which firefighters operate. Some of the possible solutions for our problem were rejected at the survey phase of related works, as is explained in Section 1. However, there still remains a set of applicable approaches. Nevertheless, we decided to develop another one, still having a chance to benefit from the existing ones as necessary. We also decided not to repeat the respected test proposed by Microsoft for indoor navigation competition at International Conference on Information Processing in Sensor Networks (IPSN) ([[https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018](https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018)]([https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018](https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018))). Since the competition test-bed is set up in large open spaces, we would not be able to test and implement our algorithm. We did not, however, compare the other algorithms in our test cases, which is undoubtedly a shortcoming of our work. The quantitative comparison with other algorithms will form part of future work. Notwithstanding, a rough comparison of our results with IPSN competitors seems to confirm that there is still a place for our solution. As is presented in the Results section, the algorithm reduces the error generated by our INS. In the best case, the error was reduced by almost 80%. However, in order for the algorithm to work efficiently, it needs proximity to the obstacle. This can be observed in Test I, where the agents moved along the middle of the corridor at some distance from the walls. In this case, the error reduction level was negligible. This naturally limits the scope of application of this algorithm--movement next to obstacles. This type of movement is typical for firefighters during search and rescue operations. The algorithm also significantly reduces the maximum error. An additive error (the error that increases with distance travelled) is typical for INS. Generic INS achieves the maximum error close to seven meters, while our error correction technique keeps the maximum error close to two meters (when an agents moves along the walls). In the future, we are going to gather more experimental data, in order to apply a genetic algorithm to define the ranges of angles accepted for position correction, door selection and transition to other compartments. The applied ORCA algorithm has a number of parameters to be set. Manipulation of these parameters may further improve the performance of our algorithm. At the current phase, these parameters were set experimentally in constraints to ensure the accuracy of the algorithm. The definition of these parameters is also in the scope of machine learning optimization, planned for future work. Our primary goal within the project was to develop an indoor navigation system for firefighters. Therefore, we also tested the impact that our system may have on the safety of firefighters. We carried out fire-ground marked tests with the participation of firefighters. The goal of the tests was to compare the firefighters' withdrawal times from the building with and without our navigation system. The tests showed that the withdrawal time of the group with the navigation system was almost half that of the group without the navigation system. The system significantly improves withdrawal times. However, this type of test does not explicitly show the impact of our algorithm for the withdrawal time.