A vector-indexed generalization of the unsigned Stirling numbers of the first kind, defined through a k-dimensional color-board problem, comes with a recurrence, an explicit upper bound, and a concentration estimate.
Ground Texture Based Localization Using Compact Binary Descriptors
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abstract
Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of its current whereabouts, but it can also take advantage of a prior pose estimate to reduce computation time significantly. Our method is based on a novel matching strategy, which we call identity matching, that is based on compact binary feature descriptors. Identity matching treats pairs of features as matches only if their descriptors are identical. While other methods for global localization are faster to compute, our method reaches higher localization success rates, and can switch to local localization after the initial localization.
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2024 1verdicts
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C sequential optimization numbers
A vector-indexed generalization of the unsigned Stirling numbers of the first kind, defined through a k-dimensional color-board problem, comes with a recurrence, an explicit upper bound, and a concentration estimate.