Cloud-based realtime robotic Visual SLAM

Benavidez, Patrick; Muppidi, Mohan; Rad, Paul; Prevost, John J.; Jamshidi, Mo; Brown, Lutcher

doi:10.1109/syscon.2015.7116844

Cited by 41 publications

(13 citation statements)

References 8 publications

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“…Klein and Murray [16] presented a seminal work in visual SLAM, which splits the system into two parallel tasks, tracking and mapping. This framework is widely applied to speed up real-time visual SLAM systems [11], [18], [19], [26], [45], and is extended to the use of computationally expensive optimization techniques. Grisetti et al explained a graph SLAM approach in [17], in which involved to build a graph to constrain the connect camera poses from sensor measurement, and optimized in a nonlinear framework.…”

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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Section: Related Workmentioning

confidence: 99%

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

2020

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“…Offloading Simultaneous Localization and Mapping (SLAM) tasks to Edge and Fog layers has only been considered recently while Cloud-based SLAM has been studied for the last decade. Benavidez et al deployed an instance of the Robot Operating System in the Cloud to increase the computational capabilities of constraint robots for Visual SLAM (VSLAM) [13]. They pointed how this helps to overcome traditional bottlenecks in robots with limited computational resources with VSLAM performing feature identification and matching which usually need large databases.…”

Section: Related Workmentioning

confidence: 99%

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Sarker

Queralta

Gia

et al. 2019

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

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Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

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“…Cloud computing is a key enabler for solving these challenges. The idea is to design and create the architecture in which the multi-robot system uses the benefits of converged infrastructure and the resources of the cloud (information, memory, communication, and other) [32][33][34][35][36]. In practice, this usually means that on the cloud level gathering of information and their processing is performed, while the robots, as cloud service users, obtain only data and commands necessary for a direct execution of the allocated tasks.…”

Section: System Overviewmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

2019

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The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.

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Cloud-based realtime robotic Visual SLAM

Cited by 41 publications

References 8 publications

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

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

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

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