Indoor Localization Based on Fusion of AprilTag and Adaptive Monte Carlo

Ordinary wheeled mobile robots use odometry and lidar to achieve indoor localization, but the localization accuracy of this method will be affected by odometry error. Considering that the AprilTag has good real-time performance and high local positioning accuracy, this paper proposes an Adaptive Mon...

Full description

Saved in:
Bibliographic Details
Main Authors: Yu, Lei, Li, Mengning, Pan, Guangyao
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
AMCL
AprilTag
Cameras
Conferences
indoor localization
Laser radar
Location awareness
mobile robots
Monte Carlo methods
Robot vision systems
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ordinary wheeled mobile robots use odometry and lidar to achieve indoor localization, but the localization accuracy of this method will be affected by odometry error. Considering that the AprilTag has good real-time performance and high local positioning accuracy, this paper proposes an Adaptive Monte Carlo Localization (AMCL) algorithm integrated with AprilTag. Take AMCL as the global localization framework, when the camera recognizes the tag, the absolute poses obtained by AprilTag are used to correct the error of the odometry model, improve global localization accuracy through local pose correction. Finally, a mobile robot is used for the actual field test, and the results show that the localization accuracy and stability are significantly improved after fusing AprilTag compared with the AMCL.
ISSN:2693-3128
DOI:10.1109/ITNEC52019.2021.9587205