Localization is one of the most important capabilities of mobile robots. Thanks to the fast development of embedded computing hardware in recent years, many localization solutions, such as simultaneous localization and mapping (SLAM), have been vastly investigated. However, popular localization solutions rely heavily on the working environment and are not applicable to scenarios such as search and rescue in the wild, where the working environment is not accessible before the localization operation or where the environment lacks information on features and textures. The thesis thus proposes a design for an innovative localization sensor and a collaborative pose estimation scheme using the localization sensor in order to alleviate the reliance on information from the environment, while providing reliable and accurate pose estimates for mobile robots.

The proposed collaborative pose estimation scheme is comprised of individual and collaborative landmark position estimation, localization sensor inter-calibration, and collaborative sensor pose estimation, among which the inter-calibration process ensures that the sensor provides capability to also estimate orientations. In the collaborative scheme, multiple instances of the proposed sensor collaborate to estimate their respective poses by measuring the relative distance and angle among them, where the measurement errors are characterized as Gaussian white noise. Two instances of the proposed localization sensor are implemented, and the collaborative scheme is tested with the instances in the thesis. Both sensor instances reliably and accurately estimate the position of a stationary landmark, and it is demonstrated that the collaboratively estimated position estimate is more accurate than its individual counterpart. Additionally, the two instances also demonstrate their ability to track and estimate the position of a moving landmark. Lastly, the inter-calibration is experimentally validated with the instances with satisfactory performance. The experimental results presented in this work confirm the feasibility and usability of the proposed collaborative pose estimation scheme in a wide range of robotic applications.