2017 IEEE International Conference on Robotics and Automation (ICRA) 2017
DOI: 10.1109/icra.2017.7989358
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Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields

Abstract: Building large-scale 2.5D maps when spatial correlations are considered can be quite expensive, but there are clear advantages when fusing data. While optimal submapping strategies have been explored previously in covariance-form using Gaussian Process for large-scale mapping, this paper focuses on transferring such concepts into information form. By exploiting the conditional independence property of the Gaussian Markov Random Field (GMRF) models, we propose a submapping approach to build a nearly optimal glo… Show more

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