Moving Horizon Estimation for Vehicle Robots using Partial Marker Information of Motion Capture System

13th International Conference on Motion and Vibration Control; 12th International Conference on Recent Advances in Structural Dynamics

Takahashi, Manami^1 ; Nonaka, Kenichiro^2 ; Sekiguchi, Kazuma^2

Graduate School of Engineering, Tokyo City University, 1-28-1, Tamazutsumi, Tokyo, Setagaya

158-8557, Japan^1

Faculty of Engineering, Tokyo City University, 1-28-1, Tamazutsumi, Tokyo, Setagaya

158-8557, Japan^2

关键词: Heading angles;    Motion capture;    Motion capture system;    Moving horizon estimation;    Objective functions;    Robot dynamics;    Robust estimation method;    Vehicle robots;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/744/1/012049/pdf
DOI  :  10.1088/1742-6596/744/1/012049

来源: IOP

【 摘 要 】

The measurement using a motion capture camera is fluctuated by white noise and outliers. In addition, markers to be measured are frequently hidden from cameras by occlusion, then the position and heading angle of a vehicle cannot be uniquely determined because of failure to detect sufficient number of markers. Thus, robust estimation method is required which suppresses the influence of the white noise, the outlier and the occlusion. In this study, we introduce Moving Horizon Estimation (MHE) using partial marker information of motion capture system. It optimizes the objective function using both the marker information in the evaluation range and the constraints on the robot dynamics. By virtue of introduction of constraints, even if the cameras fail to measure the actual state of the robot, the estimated value is determined by MHE. It is the difference from our previous research which assumed that sufficient number of markers are available. In this paper, we estimate the position of the vehicle robot by MHE using the information of the measured markers on the robot, even if several markers are hidden. We will prove the effectiveness of the proposed method by comparing MHE with EKF.