Swarm Robot SLAM with Point-Line Matching in Weak Information Environments

Simultaneous Localization and Mapping (SLAM) in weak information environment scenarios where there are only sparse and ambiguous references are available presents significant challenges. This paper introduces a swarm robots based point-line matching based solution to enhance the accuracy and robustness of localization and mapping within such environments. Our approach employs a centralized serverbased architecture for the swarm robot system and utilizes an ORB-based Distributed Bag-of-Words (DBoW) algorithm to construct point and line feature dictionaries. By generating point-line pairs, the proposed method effectively addresses perceptual overlap issues, thereby improving the overall performance of localization and mapping for swarm robots. Experimental results validate the superiority of the proposed approach in comparison to traditional methods, highlighting its potential for real-world applications in challenging environments for swarm robot SLAM.