Adaptive Robust Kernels for Non-Linear Least Squares Problems
Adaptive Robust Kernels for Non-Linear Least Squares Problems
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DOI：
10.1109/lra.2021.3061331
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发表时间：
2020-04
影响因子：
5.2
通讯作者：
Nived Chebrolu;T. Läbe;O. Vysotska;J. Behley;C. Stachniss
中科院分区：
文献类型：
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作者：
Nived Chebrolu;T. Läbe;O. Vysotska;J. Behley;C. Stachniss
状态估计是大多数机器人系统中的关键组成部分。通常，状态估计使用某种形式的最小二乘最小化来执行。基本上，所有适用于真实世界数据的误差最小化程序都使用鲁棒内核作为处理数据中异常值的标准方法。然而，这些内核通常是手工挑选的，有时是不同的组合，并且它们的参数需要针对特定问题手动调整。在这封信中，我们建议使用一个广义的强大的内核家庭，这是自动调整的残差分布的基础上，包括常见的m-估计。我们测试了我们的自适应内核与机器人技术中两个流行的估计问题，即ICP和光束法平差。在这封信中提出的实验表明，我们的方法提供了更高的鲁棒性，同时避免了手动调整的内核参数。
State estimation is a key ingredient in most robotic systems. Often, state estimation is performed using some form of least squares minimization. Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data. These kernels, however, are often hand-picked, sometimes in different combinations, and their parameters need to be tuned manually for a particular problem. In this letter, we propose the use of a generalized robust kernel family, which is automatically tuned based on the distribution of the residuals and includes the common m-estimators. We tested our adaptive kernel with two popular estimation problems in robotics, namely ICP and bundle adjustment. The experiments presented in this letter suggest that our approach provides higher robustness while avoiding a manual tuning of the kernel parameters.