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Simultaneous Localization and Mapping (SLAM) aims to estimate the maximum likelihood map and robot pose based on a robot's control and sensor measurements. In structured environments, such as human environments, we might have additional domain knowledge that could be applied to produce higher quality mapping results. We present a method for using virtual measurements, which are measurements between...
Robot localization and mapping is an important function of determining the robot pose in an unknown environment. This paper studies the pose estimation by integrating the sensing information both from proprioceptive and exteroceptive sensors. Specifically, the iterative closest point (ICP) algorithm is utilized and modified for characterizing the feature matching of environmental information obtained...
Our sensor selection algorithm targets the problem of global self-localization of multi-sensor mobile robots. The algorithm builds on the probabilistic reasoning using Bayes filters to estimate sensor measurement uncertainty and sensor validity in robot localization. For quantifying measurement uncertainty we score the Bayesian belief probability density using a model selection criterion, and for...
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