Trait sample_consensus::Estimator

pub trait Estimator<Data> {
    type Model: Model<Data>;
    type ModelIter: IntoIterator<Item = Self::Model>;

    const MIN_SAMPLES: usize;

    fn estimate<I>(&self, data: I) -> Self::ModelIter
    where
        I: Iterator<Item = Data> + Clone;
}

An Estimator is able to create a model that best fits a set of data. It is also able to determine the residual error each data point contributes in relation to the model.

Associated Types

type Model: Model<Data>

Model is the model which is estimated from the underlying data

type ModelIter: IntoIterator<Item = Self::Model>

Iterator over the models produced from the data.

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Associated Constants

const MIN_SAMPLES: usize

The minimum number of samples that the estimator can estimate a model from.

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Required methods

fn estimate<I>(&self, data: I) -> Self::ModelIter where
    I: Iterator<Item = Data> + Clone, 

Takes in an iterator over the data and produces a model that best fits the data.

This must be passed at least Self::MIN_SAMPLES data points, otherwise estimate should panic to indicate a developer error.

None should be returned only if a model is impossible to estimate based on the data. For instance, if a particle has greater than infinite mass, a point is detected behind a camera, an equation has an imaginary answer, or non-causal events happen, then a model may not be produced.

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Implementors

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