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Related Experiment Video

Updated: Sep 3, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Local-To-Global Hypotheses for Robust Robot Localization.

R W M Hendrikx¹, H Bruyninckx^1,2,3, J Elfring¹

¹Control Systems Technology, Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.

Frontiers in Robotics and AI

|July 25, 2022

Summary

This study introduces a new global localization strategy for mobile robots using symbolic associations with geometric features. This method improves robot localization accuracy and explainability by managing fewer, more powerful data association hypotheses.

Keywords:

AGV AMR global indoor lidar localization multi-hypothesis

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Current robot localization methods often lack control over hypothesis generation and management.
Existing techniques struggle to effectively utilize feature associations for robust localization.

Purpose of the Study:

To develop a global localization strategy for mobile robots using explicit symbolic associations with annotated geometric features.
To improve the efficiency, explainability, and control of robot localization hypotheses.

Main Methods:

A novel global localization strategy employing symbolic associations between local and global map features.
Construction of an association tree to manage distinct data association hypotheses.
A registration step to validate association hypotheses for a 2D LiDAR-equipped robot.

Main Results:

The proposed method demonstrates superior performance compared to a particle filter in real-world environments.
Maintaining a smaller set of data association hypotheses leads to enhanced performance and explainability.
The strategy effectively localizes robots using fewer, more descriptive hypotheses.

Conclusions:

Explicit symbolic associations offer a more controlled and explainable approach to robot global localization.
The developed method provides a robust alternative to traditional particle filter localization techniques.
This strategy enhances mobile robot navigation in complex indoor environments.