CoVOR-SLAM：使用视觉里程计和测距的多机器人系统协同SLAM
CoVOR-SLAM: Cooperative SLAM using Visual Odometry and Ranges for Multi-Robot Systems
AI总结：
本文提出CoVOR-SLAM，一种利用视觉里程计和测距测量的协同SLAM方法，通过仅交换位姿、协方差和测距信息，显著降低多机器人系统的计算与通信负载，同时保持高精度的位姿估计。
摘要：
机器人集群相比单个机器人具有优势，因为它能更快地探索更大区域，且对单点故障更具鲁棒性。为成功执行协作任务且避免碰撞，准确的相对定位是必要的。当使用视觉同时定位与建图（VSLAM）来估计每个机器人的位姿时，通常应用智能体间闭环来减少相对定位误差。该技术利用不同机器人共同观测到的特征点来缓解误差。然而，检测智能体间闭环以及处理多个智能体传输的数据需要显著的计算和通信能力。在本文中，我们提出了使用视觉里程计和测距测量的协同SLAM（CoVOR-SLAM）来克服这一挑战。在CoVOR-SLAM框架中，机器人仅需交换位姿估计、估计的协方差（不确定性）以及机器人之间的测距测量值。由于CoVOR-SLAM不需要关联不同智能体观测到的视觉特征和地图点，计算和通信负载显著降低。所需的测距测量值可通过通信系统的导频信号获取，无需复杂的基础设施。我们使用真实图像以及两台漫游车获取的基于超宽带的真实测距数据测试了CoVOR-SLAM。此外，利用公开图像数据集和通过真实仿真生成的测距数据，在更大规模的多智能体设置下对CoVOR-SLAM进行了评估。结果表明，CoVOR-SLAM能准确估计机器人位姿，且所需的计算能力和通信能力远低于智能体间闭环技术。
英文摘要：
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.
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