Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Despite its desirable traits, such as precision and high material penetrability, the resolution of non-line-of-sight (NLOS) signals remains a very hard problem and has a significant impact on the localization accuracy. In this work, we address the peculiarities of UWB error behavior by building models that capture the spatiality as well as the multimodal nature of the error statistics. Our framework utilizes tessellated maps that associate multimodal probabilistic error models to localities in space. In addition to our UWB localization strategy (which provides absolute position estimates), we investigate the effects of collaboration in the form of relative positioning. We test our approach experimentally on a group of ten mobile robots equipped with UWB emitters and extension modules providing inter-robot relative range and bearing measurements.
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