Efficient State Estimation with Constrained Rao-Blackwellized Particle Filter

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.

Authors

• Shuai Li 0015
• Siwei Lyu
• Jeffrey C. Trinkle

Keywords

• Robot sensing systems
• Mathematical model
• Kalman filters
• State estimation
• Dynamics
• Particle Filter
• Rao-Blackwellized Particle Filter
• Efficient State Estimation
• System Dynamics
• Kalman Filter
• Filtering Method
• Physical Constraints
• Quadratic Programming
• Objective Conditions
• Robot Manipulator
• Contact Conditions
• Quadratic Programming Problem
• Robotic Tasks
• Limitations Of Sensors
• Object Pose
• Multibody System
• Linear Model
• Time Step
• Dynamic Model
• Objective Measures
• Equality Constraints
• Inequality Constraints
• Noisy Measurements
• State Estimation Problem
• Continuous State
• Robotic Gripper
• Particle Weight
• Velocity Trajectories
• Continuous State Space
• Element Of Vector