  In this paper, we present an algorithm to approximate a set of data points
with G1 continuous arcs, using points' covariance data. To the best of our
knowledge, previous arc spline approximation approaches assumed that all data
points contribute equally (i.e. have the same weights) during the approximation
process. However, this assumption may cause serious instability in the
algorithm, if the collected data contains outliers. To resolve this issue, a
robust method for arc spline approximation is suggested in this work, assuming
that the 2D covariance for each data point is given. Starting with the
definition of models and parameters for single arc approximation, the framework
is extended to multiple-arc approximation for general usage. Then the proposed
algorithm is verified using generated noisy data and real-world collected data
via vehicle experiment in Sejong City, South Korea.
