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.

翻译：本文提出一种通过学习两个参数以更好拟合残差分布的方法，提升现有鲁棒估计算法的自适应性。所分析方法利用这两个参数为迭代重加权最小二乘（Iterative Re-weighted Least Squares）计算权重。这种权重的自适应特性在测量噪声水平变化的情况下具有实用价值。我们首先在合成数据集点云配准问题和开源真实数据集LiDAR里程计上测试算法。结果表明，现有方法需要额外手动调整残差尺度参数，而我们的方法可直接从数据中学习该参数，且性能相当或更优。我们进一步提出解耦尺度参数与形状参数的思路以提升算法性能。通过详细分析算法并与文献中同类知名算法对比，我们展示了所提方法的优势。