A mobile robot needs to know its pose (position and orientation) in order to navigate and perform useful tasks. The problem of determining this pose with respect to a global or local frame is called localization, and is a key component in providing autonomy to mobile robots. Thus, localization answers the question Where am I? from the robot’s perspective.

Localization involving a single robot is a widely explored and documented problem in mobile robotics. The basic idea behind most documented localization techniques involves the optimum combination of noisy and uncertain information that comes from various robot’s sensors. However, many complex robotic applications require multiple robots to work together and share information among themselves in order to successfully and efficiently accomplish certain tasks. This leads to research in collaborative localization involving multiple robots. Several studies have shown that when multiple robots collaborativelly localize themselves, the resulting accuracy in their estimated positions and orientations outperforms that of a single robot, especially in scenarios where robots do not have access to information about their surrounding environment.

This thesis presents the main theme of most of the existing collaborative, multi-robot localization solutions, and proposes an alternative or complementary solution to some of the existing challenges in multirobot localization. Specifically, in this thesis, a heuristically tuned Extended Kalman Filter is proposed to localize a group of mobile robots. Simulations show that when certain conditions are met, the proposed tuning method significantly improves the accuracy and reliability of poses estimated by the Extended Kalman Filter. Real world experiments performed on custom-made robotic platforms validate the simulation results.