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基于M-BFSI的快速和高精度的多机器人环境建模:双向过和场景识别方法

Dai-Ming Liu¹, Jia-Shan Cui¹, Yong-Jian Zhong²

¹School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China.

|May 6, 2024

概括

本研究引入了一种新的多机器人环境建模方法,使用双向过和场景识别. 这种方法增强了协作同时定位和绘图 (SLAM) 以实现高精度的环境重建.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

多机器人系统需要准确的环境建模,以实现有效的协作.
同时本地化和映射 (SLAM) 的挑战包括由于大量旋转而导致的特征跟踪故障.
现有的方法可能会在大规模,动态环境和数据关联方面扎.

研究的目的:

设计和实施一个快速,高精度的多机器人环境建模方法.
为了解决由大角度旋转引起的多机器人SLAM中的特征跟踪故障.
为了实现高效的场景识别和跨多个机器人的合作映射.

主要方法:

集成了一种双向过机制,以增强错误匹配消除算法.
使用预先训练的字典的全球关键框架数据库将图像转换为词包向量,以有效地检索场景.
合作SLAM通过相似度得分计算,最佳图像匹配和转换矩阵应用来实现.

主要成果:

拟议的算法有效地关闭了亚机器人的预测轨迹,确保了本地化准确性.
通过实验验证证明了高精度的协作环境建模.
该方法在识别和利用不同机器人的相似场景方面表现出强大.

结论:

开发的方法为多机器人环境建模提供了强大而准确的解决方案.
双向过和场景识别显著提高了协作SLAM的性能.
这种方法为多机器人系统在复杂环境中提供了高效和精确的映射.

关键词:

自动化自动化自动化自动化自动化控制工程控制工程控制工程机器人技术机器人技术机器人技术

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