For my master’s thesis, I developed a novel indoor localization approach that can achieve accurate localization results. Indoor localization is a well-known topic of research, and many attempts have been made to find the so-called holy grail. For a robotic car swarm to be able to execute a specific task in a specific room, each robot in the swarm needs to know its own location with respect to a map of the building. Allowing the robot to deduce this location himself allows for total automation of the swarm and helps mitigate errors along the way due to faulty sensor readings. Numerous works have tried to solve this problem by using beacons or heavily trained machine learning networks. These methods, however, prevent the robot from working everywhere, as either the location needs to be adapted or the robot needs to be trained for the location. To mitigate these issues, the work of this thesis is focused on achieving indoor localization that works everywhere by researching if a combination of a particle filter and sound source localization techniques can achieve high-accuracy indoor localization.

To achieve this, particle filter and source localization techniques are combined based on probability theory. Here, so-called localization tables are used to estimate the robot’s position based on the geo- metrical properties of the map, the DOA, and the distance from a received message. This table is then used to update the position and weight of the particles, which can then be used to make an educated guess of the robot’s position. This approach proved to be effective, as it was able to achieve average RMSE results as low as 3.83 cm, where the robot only needed to drive 363 cm and localization was achieved within 9.52 seconds. The research showed that deploying more cars in the swarm leads to better results, as fewer transmissions are needed, and less distance needs to be traveled. In addition, the research also showed that localization can be achieved while driving even less distance by sharing the localization tables between the robots. This, however, does come at the cost of localization accu- racy, resulting in an RMSE of 19.89 cm while driving only 158 cm. To conclude this research, based on the results of the novel fusion of a particle filter and sound source localization techniques, it can be concluded that the work in this thesis offers a great contribution to the field of indoor localization using audio-based signals.