News

June 8, 2020 Lab website is out!

Recent Publications

Ali J. Ben Ali, Zakieh Sadat Hashemifar, and Karthik Dantu
Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, 2020. pp. 325–337.
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Ali J. Ben Ali, Sofiya Semenova, and Karthik Dantu
International Workshop on Mobile Computing Systems and Applications (HotMobile), feb 2019. pp. 163.
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Charuvahan Adhivarahan and Karthik Dantu
International Conference on Robotics and Automation (ICRA), may 2019. pp. 8026–8033.
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Zakieh S. Hashemifar, Charuvahan Adhivarahan, Anand Balakrishnan, and Karthik Dantu
Autonomous Robots, 2019. pp. 2245–2260
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Gesto: Mapping UI Events to Gestures and Voice Commands
Best Paper Honorable Mention Award
Chang Min Park, Taeyeon Ki, Ali J. Ben Ali, Nikhil Sunil Pawar, Karthik Dantu, Steven Y. Ko, and Lukasz Ziarek
Proceedings of the ACM on Human-Computer Interaction (PACM HCI), jun 2019.
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Projects

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Edge-SLAM
Edge-SLAM is an edge-assisted visual simultaneous localization and mapping. Edge-SLAM adapts Visual-SLAM into edge computing architecture to enable long operation of Visual-SLAM on mobile devices. This is achieved by offloading the computation-intensive modules to the edge. Thus, Edge-SLAM reduces resource usage on the mobile device and keeps it constant. Edge-SLAM is implemented on top of ORB-SLAM2 and is publicly available on GitHub.
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WISDOM: WIreless Sensing-assisted Distributed Online Mapping
Use wireless access points and a modified ICP algorithm to efficiently merge visual 2D and 3D maps of indoor environments from multiple robots. Received signal strength values from multiple Access Points are used to find a coarse transformation between the robots.
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Minimum Energy Coverage Path Planning for UAVs
A new algorithm for coverage path planning (CPP) problem for unmanned aerial vehicles (UAV), based on minimizing total energy consumed during flight.
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OS-based Energy Accounting for Asynchronous Resources in IoT Devices
A new mechanism to accurately account for the asynchronous energy usage of resources in mobile systems and IoT devices. By accurately relating the application requests with kernel requests to device and corresponding device responses, we can accurately attribute time of use to the requesting process.
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Augmenting Visual SLAM with Wi-Fi Sensing For Indoor Applications
Utilize Wi-Fi received signal strength to improve loop closures in visual SLAM systems in repetitive indoor environments with perceptual aliasing. This project proposes a generic way integrate Wi-Fi sensing into visual SLAM algorithms improving the accuracy of visual SLAM algorithms by 11% and reducing computation time by 15% to 25%.