  In this article, we present a method for increasing adaptivity of an existing
robust estimation algorithm by learning two parameters to better fit the
residual distribution. The analyzed method uses these two parameters to
calculate weights for Iterative Re-weighted Least Squares. This adaptive nature
of the weights can be helpful in situations where the noise level varies in the
measurements. We test our algorithm first on the point cloud registration
problem with synthetic data sets and LiDAR odometry with open source real-world
data sets. We show that the existing approach needs an additional manual tuning
of a residual scale parameter which our method directly learns from data and
has similar or better performance. We further present the idea of decoupling
scale and shape parameters to improve performance of the algorithm. We give
detailed analysis of our algorithm along with its comparison with similar
well-known algorithms from literature to show the benefits of the proposed
approach.
