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：在本文中,我们通过学习两个参数来更好地适应剩余分布,来考虑提高现有稳健估算算法适应性的好处。我们的方法使用这两个参数来计算迭代再加权最小平方(IRLS)的重量。当测量的噪音水平不同时,重量的这种适应性会有所帮助。我们首先用合成数据集来测试我们的算法,用开放源真实世界数据集来测试点云登记问题,用开放源码的实时数据集来测试Lidaroodorization。我们表明,现有方法需要用手工对一个剩余尺度参数进行额外的调整,我们的方法直接从数据中学习,并且具有类似或更好的性能。