In this letter, we increase adaptivity of an existing robust estimation algorithm by learning two parameters instead of one 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 is shown to be helpful in situations where the noise level varies in the measurements, and is shown to increase robustness to outliers. We test our algorithm first on the point cloud registration problem with synthetic data sets, where the truth transformation is known. Next, we also evaluate the approach with an open-source LiDAR-inertial SLAM package to demonstrate that the proposed approach is more effective than existing versions of the algorithm for the application of incremental LiDAR-inertial odometry. We also analyze the joint variability of the two parameters learned from the data sets.

翻译：在这封信中,我们通过学习两个参数而不是一个来更好地适应剩余分布,提高了现有稳健估算算法的适应性。我们的方法使用这两个参数来计算循环再加权最低平方(IRLS)的权重。在噪音水平在测量中各不相同的情况下,加权的这种适应性被证明是有益的,并显示能提高外部线的稳健性。我们首先用已知真相变异的合成数据集来测试点云登记问题。接下来,我们还用一个开放源的LiDAR- nearrtial SLM 软件包来评估这一方法,以证明拟议方法比现有应用递增LIDAR-肾脏测量法的算法版本更有效。我们还分析了从数据集中得出的两个参数的共同变异性。