Experimental Verification of a Vehicle Localization based on Moving Horizon Estimation Integrating LRS and Odometry

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

Sakaeta, Kuniyuki^1 ; Nonaka, Kenichiro^2 ; Sekiguchi, Kazuma^2

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

158-8857, Japan^1

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

158-8857, Japan^2

关键词: Cumulative errors;    Experimental verification;    External sensors;    Internal sensors;    Laser range sensors;    Localization method;    Moving horizon estimation;    Vehicle localization;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/744/1/012048/pdf
DOI  :  10.1088/1742-6596/744/1/012048

来源: IOP

【 摘 要 】

Localization is an important function for the robots to complete various tasks. For localization, both internal and external sensors are used generally. The odometry is widely used as the method based on the internal sensors, but it suffers from cumulative errors. In the method using the laser range sensor (LRS) which is a kind of external sensor, the estimation accuracy is affected by the number of available measurement data. In our previous study, we applied moving horizon estimation (MHE) to the vehicle localization for integrating the LRS measurement data and the odometry information where the weightings of them are balanced relatively adapting to the number of the available LRS measurement data. In this paper, the effectiveness of the proposed localization method is verified through both numerical simulations and experiments using a 1/10 scale vehicle. The verification is conducted in the situations where the vehicle position cannot be localized uniquely on a certain direction using the LRS measurement data only. We achieve accurate localization even in such a situation by integrating the odometry and LRS based on MHE. We also show the superiority of the method through comparisons with a method using extended Kalman filter (EKF).