Cloud-based robot grasping with the google object recognition engine

Kehoe, Ben; Matsukawa, Akihiro; Candido, Sal; Kuffner, James; Goldberg, Ken

doi:10.1109/icra.2013.6631180

Cited by 179 publications

(100 citation statements)

References 30 publications

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“…Big Data can also facilitate learning in Computer Vision, by matching sensor data to 3D models in an online database. [14] The RoboEarth project stores data related to objects maps, and tasks, for applications ranging from object recognition to mobile navigation to grasping and manipulation (see Figure 1) [16].…”

Section: A Big-datamentioning

confidence: 99%

IoT and Robotics: a synergy

Chowdhury¹

2017

Preprint

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Abstract-The Internet of Robotic Things (IoRT) [9] is a concept first introduced by Dan Kara at ABI Research, which talks about augmenting the existing IoT with active sensorization; thereby, opening the doors to novel business ideas, at the intersection of both IoT and Robotics. This position paper considers the synergy between IoT and robotics: it talks about the technologies in IoT that would benefit the robotics domain. The advent of Cloud Robotics and its role in aiding robot functions like sensing, manipulation, and mobility. The paper then discusses the ways in which robots can extend the capabilities of existing IoT infrastructure by acting as a special class of edge device. IoT-aided robotic applications are discussed in various domains like health-care, military, industrial plants and rescue operations. The paper concludes by considering the use case of an Intelligent Transportation System endowed by an IoRT-inspired architecture.

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Section: A Big-datamentioning

confidence: 99%

IoT and Robotics: a synergy

Chowdhury¹

2017

Preprint

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“…The Cloud Based Robot Grasping project was developed to facilitate the sharing and learning of objects and grasping functions [4]. The project allowed a robot to send a picture of an object to the cloud for processing, which would attempt to identify it within its database, returning the information on the item if found.…”

Section: Cloud Based Robot Graspingmentioning

confidence: 99%

A Rapid Deployment Big Data Computing Platform for Cloud Robotics

Duggan¹,

Dowzard²,

Katupitiya³

et al. 2017

IJCNC

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“…For example, some papers [25,27] deploy a recognition system that allows clients to upload images to the server for object classification. Other papers [20,14] provide continuous object recognition on mobile devices by running the tracker on the mobile client, and the recognition on a server.…”

Section: Related Workmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

246

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Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers objectrecognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. Categories and Subject Descriptors

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Cloud-based robot grasping with the google object recognition engine

Cited by 179 publications

References 30 publications

IoT and Robotics: a synergy

IoT and Robotics: a synergy

A Rapid Deployment Big Data Computing Platform for Cloud Robotics

Glimpse

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