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首页> 外文期刊>Journal of Robotic Systems >CCM-SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams
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CCM-SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams

机译：CCM-SLAM：强大，有效的集中协同单眼同时定位和机器人团队的映射

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摘要

Robotic collaboration promises increased robustness and efficiency of missions with great potential in applications, such as search-and-rescue and agriculture. Multiagent collaborative simultaneous localization and mapping (SLAM) is right at the core of enabling collaboration, such that each agent can colocalize in and build a map of the workspace. The key challenges at the heart of this problem, however, lie with robust communication, efficient data management, and effective sharing of information among the agents. To this end, here we present CCM-SLAM, a centralized collaborative SLAM framework for robotic agents, each equipped with a monocular camera, a communication unit, and a small processing board. With each agent able to run visual odometry onboard, CCM-SLAM ensures their autonomy as individuals, while a central server with potentially bigger computational capacity enables their collaboration by collecting all their experiences, merging and optimizing their maps, or disseminating information back to them, where appropriate. An in-depth analysis on benchmarking datasets addresses the scalability and the robustness of CCM-SLAM to information loss and communication delays commonly occurring during real missions. This reveals that in the worst case of communication loss, collaboration is affected, but not the autonomy of the agents. Finally, the practicality of the proposed framework is demonstrated with real flights of three small aircraft equipped with different sensors and computational capabilities onboard and a standard laptop as the server, collaboratively estimating their poses and the scene on the fly.
机译：机器人合作承诺增加了潜在潜力的特派团的稳健性和效率，例如搜救和救援和农业。 Multi8Gent协作同步定位和映射（SLAM）在启用协作的核心是正确的，使得每个代理可以在工作区中的映射和构建一个地图。然而，这个问题核心的关键挑战是稳健的通信，高效的数据管理，以及在代理商之间有效地分享信息。为此，我们在这里提出CCM-SLAM，一种用于机器人代理的集中式协作框架，每个都配备有单眼相机，通信单元和小型加工板。对于每个代理能够在板载中运行视觉内径术，CCM-Slam确保其自主性作为个体，而具有潜在更大的计算能力的中央服务器通过收集其所有经验，合并和优化其地图，或将信息传播回给他们来实现他们的协作，或者在适当情况下。关于基准测试数据集的深入分析解决了CCM-Slam对信息丢失和在真实任务期间通常发生的通信延迟的可扩展性和鲁棒性。这表明，在最坏的沟通损失的情况下，合作受到影响，但不是代理人的自主权。最后，拟议的框架的实用性是用三架小型飞机的真正飞行，配备了不同的传感器和板载和标准笔记本电脑作为服务器的标准笔记本电脑，在飞行中协同估计它们的姿势和场景。

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