Spatial perception is a key task in several machine intelligence applications such as robotics and computer vision. In general, it involves the nonlinear estimation of hidden variables that represent the system's state. However, in the presence of measurement 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 three Bayesian heuristics that have similar structures. 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. The general computational advantages our proposals offer make them attractive candidates for spatial perception tasks.

翻译：空间感知是机器人学和计算机视觉等多项机器学习应用中的关键任务。通常，它涉及对表示系统状态的隐藏变量的非线性估计。然而，在存在测量异常值的情况下，标准的非线性最小二乘公式会导致较差的估计结果。文献中已提出多种方法来提高估计过程的可靠性。大多数方法基于启发式策略，因为保证全局鲁棒估计通常因高计算成本而不切实际。近期，有研究提出了通用鲁棒估计启发式方法，这些方法利用现有无异常值公式的非最小化解算器，无需初始猜测。在本工作中，我们提出了三种结构相似的贝叶斯启发式方法。我们在实际场景中评估了这些启发式方法，以展示其在不同应用中的优势，包括三维点云配准、网格配准和位姿图优化。我们提出的方法所提供的通用计算优势使其成为空间感知任务中有吸引力的候选方案。