Enhanced Multi-Robot SLAM System with Cross-Validation Matching and Exponential Threshold Keyframe Selection

TL;DR

This paper enhances multi-robot SLAM by introducing cross-validation matching and exponential threshold keyframe selection, significantly improving accuracy and robustness over existing methods like ORB-SLAM3.

Contribution

It proposes novel filtering and keyframe selection techniques within a multi-robot SLAM framework, improving localization accuracy and mapping robustness.

Findings

12.90% reduction in absolute trajectory error

Improved positioning accuracy and mapping quality

Enhanced efficiency and robustness in multi-robot SLAM

Abstract

The evolving field of mobile robotics has indeed increased the demand for simultaneous localization and mapping (SLAM) systems. To augment the localization accuracy and mapping efficacy of SLAM, we refined the core module of the SLAM system. Within the feature matching phase, we introduced cross-validation matching to filter out mismatches. In the keyframe selection strategy, an exponential threshold function is constructed to quantify the keyframe selection process. Compared with a single robot, the multi-robot collaborative SLAM (CSLAM) system substantially improves task execution efficiency and robustness. By employing a centralized structure, we formulate a multi-robot SLAM system and design a coarse-to-fine matching approach for multi-map point cloud registration. Our system, built upon ORB-SLAM3, underwent extensive evaluation utilizing the TUM RGB-D, EuRoC MAV, and TUM_VI datasets. The experimental results demonstrate a significant improvement in the positioning accuracy and mapping quality of our enhanced algorithm compared to those of ORB-SLAM3, with a 12.90% reduction in the absolute trajectory error.