Spatial perception is a key task in several robotics applications. In general, it involves the nonlinear estimation of hidden variables that represent the state of the robot/environment. However, in the presence of outliers the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose two similar heuristics backed by Bayesian theory. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization.
翻译:空间认知是若干机器人应用中的一项关键任务。 一般而言,它涉及对代表机器人/环境状态的隐藏变量的非线性估计。 但是,在有外部线的情况下,标准的非线性最小平方配方导致估计差。 文献中考虑了几种方法来提高估算过程的可靠性。 多数方法都是基于疲劳论,因为全球可靠估算得到保证,由于计算成本高,通常不切实际。 最近,提出了通用的稳健估算,利用现有非最低溶解剂,无需初步猜测。 在这项工作中,我们提出了由贝叶斯理论支持的两种类似的超值配方。 我们评估了这些实际情景中的超值,以表明其在不同应用中的优点,包括3D点云登记、网状登记和图形优化。