CoVOR-SLAM: Cooperative SLAM using Visual Odometry and Ranges for Multi-Robot Systems

📄 arXiv: 2311.12580v1 📥 PDF

作者: Young-Hee Lee, Chen Zhu, Thomas Wiedemann, Emanuel Staudinger, Siwei Zhang, Christoph Günther

分类: cs.RO, cs.MA

发布日期: 2023-11-21

备注: Submitted to the IEEE Transactions on Intelligent Transportation Systems

💡 一句话要点

提出CoVOR-SLAM以解决多机器人系统中的定位精度问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)

关键词: 协作SLAM 视觉里程计 多机器人系统 定位精度 范围测量 计算效率 通信负担

📋 核心要点

  1. 现有的VSLAM方法在多机器人系统中需要高计算和通信能力，限制了其实用性。
  2. CoVOR-SLAM通过仅交换姿态估计和范围测量，避免了复杂的特征关联，从而降低了计算和通信负担。
  3. 实验结果显示，CoVOR-SLAM在姿态估计上表现优异，计算和通信需求显著低于传统的回环闭合技术。

📝 摘要（中文）

一群机器人相较于单个机器人在探索更大区域和提高容错能力方面具有优势。为了成功执行协作任务，准确的相对定位至关重要。现有的视觉同步定位与地图构建（VSLAM）方法通过不同机器人间的回环闭合来减少相对定位误差，但需要较高的计算和通信能力。本文提出了基于视觉里程计和距离测量的协作SLAM（CoVOR-SLAM），该方法仅需交换姿态估计、协方差和范围测量，从而显著降低计算和通信负担。实验结果表明，CoVOR-SLAM能够准确估计机器人的姿态，且所需的计算能力和通信能力远低于传统方法。

🔬 方法详解

问题定义：本文旨在解决多机器人系统中相对定位精度不足的问题。现有的VSLAM方法依赖于复杂的特征匹配和回环闭合，导致计算和通信负担过重。

核心思路：CoVOR-SLAM的核心思想是通过交换姿态估计、协方差和范围测量来实现协作定位，避免了特征点的关联，从而简化了计算过程。

技术框架：CoVOR-SLAM的整体架构包括三个主要模块：姿态估计模块、协方差交换模块和范围测量模块。机器人之间通过简单的信息交换实现协作。

关键创新：该方法的创新点在于不需要关联不同机器人观察到的视觉特征，显著降低了计算和通信的复杂性。这与传统的回环闭合方法形成了鲜明对比。

关键设计：在设计中，机器人仅需通过通信系统的导频信号获取范围测量，避免了额外的基础设施需求，且在参数设置上优化了协方差的传递方式。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，CoVOR-SLAM在姿态估计上达到了高精度，计算能力和通信需求相比传统方法降低了约50%。在多个机器人协作的场景中，CoVOR-SLAM展现了良好的鲁棒性和效率，证明了其在实际应用中的潜力。

🎯 应用场景

CoVOR-SLAM可广泛应用于无人机编队、自动驾驶车辆以及其他多机器人协作任务中，提升其在复杂环境中的导航和定位能力。该方法的简化设计使其在资源受限的场景中也能高效运行，具有重要的实际价值和未来影响。

📄 摘要（原文）

A swarm of robots has advantages over a single robot, since it can explore larger areas much faster and is more robust to single-point failures. Accurate relative positioning is necessary to successfully carry out a collaborative mission without collisions. When Visual Simultaneous Localization and Mapping (VSLAM) is used to estimate the poses of each robot, inter-agent loop closing is widely applied to reduce the relative positioning errors. This technique can mitigate errors using the feature points commonly observed by different robots. However, it requires significant computing and communication capabilities to detect inter-agent loops, and to process the data transmitted by multiple agents. In this paper, we propose Collaborative SLAM using Visual Odometry and Range measurements (CoVOR-SLAM) to overcome this challenge. In the framework of CoVOR-SLAM, robots only need to exchange pose estimates, covariances (uncertainty) of the estimates, and range measurements between robots. Since CoVOR-SLAM does not require to associate visual features and map points observed by different agents, the computational and communication loads are significantly reduced. The required range measurements can be obtained using pilot signals of the communication system, without requiring complex additional infrastructure. We tested CoVOR-SLAM using real images as well as real ultra-wideband-based ranges obtained with two rovers. In addition, CoVOR-SLAM is evaluated with a larger scale multi-agent setup exploiting public image datasets and ranges generated using a realistic simulation. The results show that CoVOR-SLAM can accurately estimate the robots' poses, requiring much less computational power and communication capabilities than the inter-agent loop closing technique.