Open AccessResearch on the Positioning of AGV Based on Lidar
Author(s) -
Yuheng Liu,
Yan Piao,
Luyuan Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1920/1/012087
Subject(s) - odometer , computer science , automated guided vehicle , inertial measurement unit , particle filter , robot , matching (statistics) , artificial intelligence , computer vision , real time computing , kalman filter , mathematics , statistics
The rapid development of the e-commerce industry has led to the transformation of the logistics industry from labor-intensive to technology-intensive. The intelligent management system has gradually eliminated the past manual operation methods. AGV (automated guided vehicle, AGV) robot is an indispensable equipment in modern intelligent production and enterprise warehousing logistics systems. In order to improve the positioning performance of the AGV robot to a certain extent, this paper proposes an optimized AMCL (Adaptive Monte Carlo Localization) positioning algorithm based on EKF data processing. First, input the odometer and IMU data into the EKF model for fusion, then the fused state is used as the motion model of the positioning algorithm to predict the pose of the particle set and assist the particle update. The AMCL output after weighted average processing is used as the initial value of scan matching. By constructing a matching function model of lidar observation points and a priori map, using Gauss Newton’s method to optimize the solution, the accuracy has been improved.