论文标题
分析针对非线性最小二乘问题的量表变化稳健内核优化
Analysis of Scale-Variant Robust Kernel Optimization for Non-linear Least Squares Problems
论文作者
论文摘要
在本文中，我们提出了一种通过学习两个参数以更好地符合剩余分布来提高现有鲁棒估计算法适应性的方法。分析的方法使用这两个参数来计算迭代重新加权最小二乘的权重。在噪声水平在测量中有所不同的情况下，权重的这种自适应性质可能会有所帮助。我们首先在与开源现实世界数据集的合成数据集和LIDAR探光仪的点云注册问题上测试我们的算法。我们表明，现有方法需要对剩余比例参数进行附加的手动调整，我们的方法直接从数据中学习并具有相似或更好的性能。我们进一步介绍了分离量表和形状参数的想法，以提高算法的性能。我们对我们的算法进行了详细的分析，以及它与文献中类似众所周知的算法的比较，以显示拟议方法的好处。
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.
