Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping

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 th...

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Veröffentlicht in:IEEE internet of things journal Jg. 9; H. 15; S. 14087 - 14102
Hauptverfasser: Huang, Peng, Zeng, Liekang, Chen, Xu, Huang, Luo, Zhou, Zhi, Yu, Shuai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Cloud computing
Computer architecture
Distributed and parallel processing
Edge computing
edge intelligence
edge offloading
Localization
Merging
Multiple robots
multirobot laser simultaneous localization and mapping (SLAM)
Robot kinematics
Robot sensing systems
Robotics
Robots
Servers
Simultaneous localization and mapping
ISSN:2327-4662, 2327-4662
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Zusammenfassung: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3146461