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This thesis by Karl Granström explores advanced methodologies in the realm of autonomous robotics, specifically focusing on Simultaneous Localization and Mapping (SLAM) and Multiple Target Tracking (MTT). The work addresses the challenges robots face in mapping environments and tracking moving objects using Probability Hypothesis Density (PHD) filters. Granström presents innovative approaches for detecting loop closures in SLAM with laser range sensors and extends target tracking by modeling targets with Gaussian mixtures and Gaussian inverse Wishart mixtures. This research is particularly relevant for applications in urban environments, where accurate tracking of pedestrians, bicycles, and vehicles is essential. The intended audience includes researchers and professionals in robotics and electrical engineering.

Robotics. --- Target acquisition. --- Robotics --- Target acquisition