Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping
Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping
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DOI：
10.1109/jiot.2022.3146461
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发表时间：
2021-12
影响因子：
10.6
通讯作者：
Peng Huang;Liekang Zeng;Xu Chen;Ke Luo;Zhi Zhou;Shuai Yu
中科院分区：
文献类型：
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作者：
Peng Huang;Liekang Zeng;Xu Chen;Ke Luo;Zhi Zhou;Shuai Yu
随着智能机器人在各个领域的广泛渗透，机器人中的同步定位与地图构建（SLAM）技术越来越受到社会各界的关注。然而，由于SLAM的密集图形计算和机器人有限的计算能力之间的性能矛盾，在多个机器人上协作SLAM仍然具有挑战性。虽然传统的解决方案诉诸于强大的云服务器作为外部计算提供商，我们表明，通过现实世界的测量数据卸载显着的通信开销，防止其实用性的真实的部署。为了应对这些挑战，本文将新兴的边缘计算范式推广到多机器人SLAM中，并提出了RecSLAM，这是一种多机器人激光SLAM系统，专注于加速机器人边缘云架构下的地图构建过程。与传统的多机器人SLAM不同，RecSLAM开发了一种分层地图融合技术，将机器人的原始数据定向到边缘服务器进行实时融合，然后发送到云端进行全局融合。为了优化整体流水线，引入了高效的多机器人SLAM协同处理框架，根据异构边缘资源条件自适应优化机器人到边缘的卸载，同时确保边缘服务器之间的工作负载平衡。大量的测试表明，RecSLAM在处理延迟方面比现有技术降低了39.31%，并在真实的场景中验证了其有效性。
With the wide penetration of smart robots in multifarious fields, the simultaneous localization and mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this article promotes the emerging edge-computing paradigm into multirobot SLAM and proposes RecSLAM, a multirobot laser SLAM system that focuses on accelerating the map construction process under the robot–edge–cloud architecture. In contrast to the conventional multirobot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots’ raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multirobot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39.31% processing latency reduction over the state of the art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness.