Relative Localization System Design for SnailBot: A Modular Self-reconfigurable Robot

TL;DR

This work tackles the problem of precise relative localization for modular SnailBot units by fusing data from ArUco marker detection, optical flow, and IMU sensors in real time. The system leverages three parallel pipelines and a rule-based fusion core that prioritizes absolute poses from ArUco when available and relies on optical flow and IMU data otherwise, achieving robust performance under occlusions and dynamic motion. Experimental results show sub-centimeter accuracy in relative pose with low outlier rates and strong repeatability, demonstrating viability for collaborative tasks and scalable deployment within indoor environments. The work provides a practical, modular approach to relative localization in self-reconfigurable robots, highlighting areas for future enhancement such as markerless methods and advanced fusion techniques.

Abstract

This paper presents the design and implementation of a relative localization system for SnailBot, a modular self reconfigurable robot. The system integrates ArUco marker recognition, optical flow analysis, and IMU data processing into a unified fusion framework, enabling robust and accurate relative positioning for collaborative robotic tasks. Experimental validation demonstrates the effectiveness of the system in realtime operation, with a rule based fusion strategy ensuring reliability across dynamic scenarios. The results highlight the potential for scalable deployment in modular robotic systems.

Figures (7)

• Figure 1: SnailBot.
• Figure 2: Overall system architecture.
• Figure 3: Bottom Camera Placement Comparison.
• Figure 4: ArUco Detection.
• Figure 5: Experiment Result.