论文标题
注意差距：多变量最小二乘问题的规范意识适应性损失
Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems
论文作者
论文摘要
在解决现实世界机器人状态估计问题时，测量异常值是不可避免的。存在大量可靠的损失功能（RLF），以减轻异常值的影响，包括新开发的自适应方法，这些方法不需要参数调整。所有这些方法都假定残差遵循零均值的高斯样分布。但是，在多元问题中，残差通常被定义为标准，规范遵循具有非零模式值的卡式分布。这会产生一个“模式差距”，从而影响现有RLF的收敛速率和准确性。提出的方法“自适应MB”通过首先使用自适应卡的分布来估算残差的模式来解释这一差距。将现有的自适应加权方案应用于大于模式的残留物，从而在两个基本状态估计问题，点云对齐和姿势平均范围内导致更强的性能和更快的收敛时间。
Measurement outliers are unavoidable when solving real-world robot state estimation problems. A large family of robust loss functions (RLFs) exists to mitigate the effects of outliers, including newly developed adaptive methods that do not require parameter tuning. All of these methods assume that residuals follow a zero-mean Gaussian-like distribution. However, in multivariate problems the residual is often defined as a norm, and norms follow a Chi-like distribution with a non-zero mode value. This produces a "mode gap" that impacts the convergence rate and accuracy of existing RLFs. The proposed approach, "Adaptive MB," accounts for this gap by first estimating the mode of the residuals using an adaptive Chi-like distribution. Applying an existing adaptive weighting scheme only to residuals greater than the mode leads to more robust performance and faster convergence times in two fundamental state estimation problems, point cloud alignment and pose averaging.
