考虑下面的类，我想从两个传递给构造函数的Eigen：：Ref初始化两个成员变量（普通的const ref）。
在构造函数内部，打印

std::cout << "Matrix Param: " << current_predicted_sigma_points << std::endl;
std::cout << "Matrix Member: " << current_predicted_sigma_points_ << std::endl;

按预期工作，它们打印相同的矩阵，这意味着current_predicted_sigma_points_已正确初始化。
然而，当我调用Update函数时，变量current_predicted_sigma_points_包含垃圾，但weights_仍然正确。

template <typename ProcessModel, typename MeasurementModel>
class UnscentedUpdateStrategy
{
  public:
    UnscentedUpdateStrategy(
        const Eigen::Ref<const ukf_utils::PredictedSigmaMatrix<ProcessModel, ProcessModel::n_sigma_points>>
            current_predicted_sigma_points,
        const Eigen::Ref<const Eigen::Vector<double, ProcessModel::n_sigma_points>> weights)
        : current_predicted_sigma_points_{current_predicted_sigma_points}, weights_{weights}
    {
        std::cout << "Matrix Param: " << current_predicted_sigma_points << std::endl;
        std::cout << "Matrix Member: " << current_predicted_sigma_points_ << std::endl;
    }

    void Update(const Eigen::Ref<const typename MeasurementModel::PredictedVector>& measure,
                const simpleukf::ukf_utils::MeanAndCovariance<ProcessModel>& current_hypotesis,
                simpleukf::ukf_utils::MeanAndCovariance<ProcessModel>& mean_and_cov_out) const
    {
        simpleukf::ukf_utils::PredictedSigmaMatrix<MeasurementModel, ProcessModel::n_sigma_points>
            measurement_predicted_sigma_matrix_out;

        std::cout << "Matrix Member Update: " << current_predicted_sigma_points_ << std::endl;
        std::cout << "Weights Update: " << weights_ << std::endl;

        auto measurement_prediction = ukf_utils::PredictMeanAndCovarianceFromSigmaPoints<MeasurementModel>(
            measurement_predicted_sigma_matrix_out, current_predicted_sigma_points_, weights_);
        // std::forward<const MeasurementPredictionArgs>(args)...);

        // add measurement noise covariance matrix
        measurement_prediction.covariance += MeasurementModel::noise_matrix_squared;
        
        // create matrix for cross correlation: predicted measurement `measurement_prediction.mean` and pred covariance
        // `measurement_prediction.covariance`
        const auto cross_correlation_matrix =
            ukf_utils::ComputeCrossCorrelation<ProcessModel, MeasurementModel, ProcessModel::n_sigma_points>(
                current_predicted_sigma_points_,
                current_hypotesis.mean,
                measurement_predicted_sigma_matrix_out,
                measurement_prediction.mean,
                weights_);

        // Kalman gain K;
        Eigen::Matrix<double, ProcessModel::n, MeasurementModel::n> K =
            cross_correlation_matrix * measurement_prediction.covariance.inverse();

        // residual
        typename MeasurementModel::PredictedVector measure_diff = measure - measurement_prediction.mean;

        // angle normalization
        models_utils::AdjustIfNeeded<MeasurementModel>(measure_diff);

        // update state mean and covariance matrix
        mean_and_cov_out.mean = current_hypotesis.mean + K * measure_diff;
        mean_and_cov_out.covariance =
            current_hypotesis.covariance - K * measurement_prediction.covariance * K.transpose();
    }

  private:
    // TODO: should we keep a copy or not?
    const ukf_utils::PredictedSigmaMatrix<ProcessModel, ProcessModel::n_sigma_points>& current_predicted_sigma_points_;
    const Eigen::Vector<double, ProcessModel::n_sigma_points>& weights_;
};

当将成员切换到Eigen：：Ref类型时，问题消失了，但我想了解在原始情况下发生了什么，因为我可能在嵌套函数接受普通引用时遇到同样的问题。