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Regression-Based Docking System for Autonomous Mobile Robots Using a Monocular Camera and ArUco Markers.

Jun Seok Oh¹, Min Young Kim^2,3,4

¹School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This study presents a low-cost autonomous charging system using a monocular camera and ArUco markers. The new regression-based method accurately estimates distance and orientation, outperforming traditional vision techniques for industrial applications.

Keywords:

ArUco markers autonomous docking monocular camera regression model

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Area of Science:

Robotics and Automation
Computer Vision
Artificial Intelligence

Background:

Traditional monocular vision systems struggle with accurate spatial estimation due to sensitivity to viewing angles, lighting, and calibration.
Existing methods like SolvePnP exhibit significant errors in distance and orientation estimation.

Purpose of the Study:

To develop a cost-effective autonomous charging docking system using a monocular camera and ArUco markers.
To improve the accuracy of spatial estimation for autonomous docking by overcoming limitations of traditional methods.

Main Methods:

A novel regression-based method is proposed, learning geometric features from ArUco marker variations (size, shape) for distance and orientation estimation.
The model is trained with ground-truth data from a LiDAR sensor.
Real-time operation relies solely on monocular camera input.

Main Results:

The proposed system achieved a mean distance error of 1.18 cm and a mean orientation error of 3.11°.
This significantly outperforms SolvePnP, which had errors of 58.54 cm and 6.64°.
Real-world docking tests demonstrated an average position error of 2 cm and orientation error of 3.07°.

Conclusions:

Accurate and reliable autonomous docking is achievable with low-cost, vision-only hardware.
The developed system offers a practical and scalable solution for industrial autonomous charging applications.
The regression-based approach effectively addresses the limitations of traditional monocular vision techniques.