Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

Zhang, Pengfei; Wang, Huaimin; Ding, Bo; Shang, Suning

doi:10.1109/icws.2018.00026

Cited by 36 publications

(12 citation statements)

References 21 publications

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“…However, the scalability validation of most existing multirobot applications, such as multirobot SLAM, is still limited to a small group of robots, which is hardly directly scaled to dozens of robots. We improve ORB-SLAM [31] for better scalability and propose a scalable and real-time multirobot visual SLAM framework [32]. This framework can effectively divide and schedule SLAM task inside a cluster, with the group-based parallelism and the map point multicut algorithm.…”

Section: Evaluation Casementioning

confidence: 99%

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Zhai

Ding

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Computation offloading has been widely recognized as an effective way to promote the capabilities of resource-constrained mobile devices. Recent years have seen a renewal of the importance of this technology in the emerging field of mobile robots, supporting resource-intensive robot applications. However, cooperating to solve complex tasks in the physical world, which is a significant feature of a robot swarm compared to traditional mobile computing devices, has not received in-depth attention in research concerned with traditional computation offloading. In this study, we propose an approach named cooperation offloading, which offloads the intensive communication among robots as well as the computation for compute-intensive and data-intensive tasks. We analyze the performance gain of cooperation offloading by formalizing multirobot cooperative models; in addition, we study offloading decisions. Based on this approach, we design a cloud robotic framework named Cloudroid Swarm and develop several QoS-aware mechanisms to provide a general solution to cooperation offloading with QoS assurance in multirobot cooperative scenes. We implement Cloudroid Swarm to transparently migrate multirobot applications to cloud servers without any code modification. We evaluate our framework using three different multirobot cooperative applications. Our results show that Cloudroid Swarm can be applied to various robotic applications and real-world environments and bring significant benefits in terms of both network optimization and task performance. Besides, our framework has good scalability and can do support as many as 256 robot entities simultaneously.

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Section: Evaluation Casementioning

confidence: 99%

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Zhai

Ding

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…Its core procedure beyond single-robot SLAM is the map fusion procedure that merges multiple grid maps to yield a global map of the targeted scene. Given that grid maps are collected from multiple distributed robots, state-of-theart solutions [17]- [19] resort to the centralized cloud server to fulfill map fusion, as illustrated in Fig. 3.…”

Section: A Multi-robot Slammentioning

confidence: 99%

“…For example, some indoor mapping services employ multiple robots to scan the house and perform multi-robot SLAM to construct a 3D graphic model that shapes the indoor environment [15]. In some geographical mapping applications, many robots are run to scan landforms and build topographic maps [17].…”

Section: A Multi-robot Slammentioning

confidence: 99%

“…Another example is in mission-critical Search and Rescue (SAR) tasks [14], [15], where multiple robots are driven collaboratively to share map data with each other for rapid exploration in broken buildings. In these circumstances, traditional wisdom [17]- [19] to reconcile the distributed data sources calls for robot-cloud synergy, where robots run SLAM individually and upload their local map mutually to a centralized cloud server for global merging. Yet the two-tier architecture suffers from dual drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Huang¹,

Zeng²,

Xu³

et al. 2021

Preprint

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With the wide penetration of smart robots in multifarious fields, 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 paper promotes the emerging edge computing paradigm into multi-robot SLAM and proposes RecSLAM, a multi-robot laser SLAM system that focuses on accelerating map construction process under the robot-edge-cloud architecture. In contrast to conventional multi-robot 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 multi-robot 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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“…The most important benefit of cloud computing is data and computing resources sharing, so using cloud computing is a trend in applications such as social robots [23]. Visual odometry [25], vision based 3D mapping [13], [26], [37], robot navigation [27], [36] and multi-robots collaboration [30], [31] are the typical applications for cloud robotics. Limosani et al presented a system in the cloud robotics paradigm to help autonomous robots navigating in indoor environment.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Cloud Visual Simultaneous Localization and Mapping for Indoor Service Robots

2020

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Unlike traditional industrial robots, indoor service robots are usually required to possess high intelligence, such as the skills of flexible moving, precise spacial perceiving. And high intelligence is always accompanied by consuming complicated and expensive computation resources. One solution for indoor service robots is centralization of expensive computation resource so that it is possible to design a low cost client with a high-intelligence brain. However, as a fundamental intelligence function for mobile indoor robots, if a real-time visual Simultaneously Localization and Mapping (vSLAM) system is split into client and brain, it will be confronted with new challenges, such as the barrier of instant data sharing and performance degradation brought by network delay inbetween. To solve the problem, we focus on a framework and approach of cloud-based visual SLAM in this paper, and provide an efficient solution to offload the expensive computation and reduce the cost of robot clients. The integrated system is distributed in a 3-level Cloud with lightweight tracking, high precision dense mapping, and map sharing. Based on recent excellent algorithms, our system is able to run a real-time sparse tracking on the client, and a real-time dense mapping on the cloud server, which outputs an explicit 3D dense map. Only keyframes are sent to the local cloud center to reduce the network bandwidth requirement. Dense geometric pose estimation besides feature-based methods is computed to make the system resistant to featureless indoor scenes. The camera poses associated with keyframes are optimized on the local computing cloud center, and are sent back to the client to decrease the trajectory drift. We evaluate the system on the Technical University of Munich (TUM) datasets, Imperial College London and National University of Ireland Maynooth (ICL-NUIM) datasets, and the real data captured by our robot in terms of visual odometry on the client side and dense maps generated on the server cloud. Qualitative and quantitative experiments show our cloud visual SLAM system is able to bear the network delay in Local Area Network (LAN), and it is an efficient vSLAM solution for indoor service robots with high intelligence from a centric brain.

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Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

Cited by 36 publications

References 21 publications

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Real-Time Cloud Visual Simultaneous Localization and Mapping for Indoor Service Robots

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