Due to the limitations of the robotic sensors, during a robotic manipulation task, the acquisition of the object's state can be unreliable and noisy. Combining an accurate model of multi-body dynamic system with Bayesian filtering methods has been shown to be able to filter out noise from the object's observed states. However, efficiency of these filtering methods suffers from samples that violate the physical constraints, e.g., no penetration constraint. In this paper, we propose a Rao-Blackwellized Particle Filter (RBPF) that samples the contact states and updates the object's poses using Kalman filters. This RBPF also enforces the physical constraints on the samples by solving a quadratic programming problem. By comparing our method with methods that does not consider physical constraints, we show that our proposed RBPF is not only able to estimate the object's states, e.g., poses, more accurately but also able to infer unobserved states, e.g., velocities, with higher precision.

翻译：由于机器人传感器的局限性，在机器人操作任务中，物体状态的获取往往不可靠且充满噪声。将多体动力系统的精确模型与贝叶斯滤波方法相结合，已被证明能够滤除物体观测状态中的噪声。然而，这些滤波方法的效率因违反物理约束（如无穿透约束）的样本而受到影响。本文提出了一种Rao-Blackwellized粒子滤波器，该方法对接触状态进行采样，并利用卡尔曼滤波器更新物体位姿。该RBPF还通过求解二次规划问题对样本施加物理约束。通过将我们的方法与不考虑物理约束的方法进行比较，我们证明所提出的RBPF不仅能更准确地估计物体状态（如位姿），还能以更高精度推断未观测状态（如速度）。