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Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy.
We characterize the various phases of the algorithm that help us to develop a theoretical framework required for analysis.
In this research, we expanded on the previous research by introducing several optimizations in the training and classification phases of the algorithm.
The reason why it is so fast is that Expedite wastes less time in mining intermediate item sets that are discarded in later phases of the algorithm.
The phases of the algorithm can be repeated, as collapsing parallel structures may lead to new sequential structures.
The Use feedback and Check functions realize the training and the change detection phases of the algorithm, respectively.
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The second compared the overall time in seconds for each of the two phases of the algorithms.
The first phase of the algorithm analyzes all the provided images and calculates the bottleneck values for each of them.
The general-purpose CPU is used in the setup phase of the algorithm and in the preprocessing of the sensors' readings.
Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions.
In particular, our control system stems from the idea of adding robustness in the prediction phase of the algorithm through a specific robust Kalman filter recently introduced.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com