In this article, we consider the benefit of increasing adaptivity of an existing robust estimation algorithm by learning two parameters to better fit the residual distribution. Our method uses these two parameters to calculate weights for Iterative Re-weighted Least Squares (IRLS). 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.

翻译：本文探讨了通过学习两个参数以更好地拟合残差分布，从而增强现有鲁棒估计算法适应性的优势。本方法利用这两个参数为迭代重加权最小二乘（IRLS）计算权重。这种权重的自适应特性在测量噪声水平变化的场景中具有实用价值。我们首先在合成数据集上的点云配准问题中测试算法，随后在开源真实世界数据集上进行激光雷达里程计测试。结果表明，现有方法需要额外手动调整残差尺度参数，而本方法可直接从数据中学习该参数，并实现相似或更优的性能表现。