Research Vision
There is an increasing demand for indoor robotics in the recent years. Robots have proved themselves to work well in constrained industrial settings and today there is a need to bring these robots to more unconstrained human spaces. And reliable operation in these messy environments will be the key to their success.
An important way to improve this much needed reliability is to develop new sensing modalities to better understand the environment. By incorporating RF-sensing, my reseach is helping push the envelope in developing new sensing modalities which can be readily integrated with today's robots. More details about my projects can be found below.
Apart from research, I enjoy spending extended periods of time cooking, hiking and sketching
Updates
- We are demoing our open sourced WiFi-sensing platform at IPSN 2023. More information about the project is here. [GitHub]
- I recently presented my work at University of Washington, Google Research and Microsoft Research. A recording of my talk is here
- We are demoing extensions on our UWB-tracking project at Mobisys 2022. Stay tuned for more information!
- I will be presenting my work, P2SLAM, at ICRA during the Tuesday morning, 05/24/22, Sensor fusion track. It was recently accepted jointly with RAL-2022
- Our WiFi sensor fusion work, P2SLAM, was covered by Science Daily, Cosmos , EurekaAlert and UCSD News. The featured video can be found here.
Projects
WiFi radios as extrinsic sensors for Robot SLAM
Extrinsic sensors, like cameras and LiDAR’s, employed for fusion can correct the the drifts accumulated by wheel odometry or inertial measurement units (IMU’s) for robot localization and mapping. However, these exteroceptive sensors are deficient in highly structured environments and dynamic lighting conditions. We present WiFi as a robust and straightforward sensing modality capable of circumventing these issues.
-
P2SLAM: Bearing based WiFi SLAM for Indoor Robots
Aditya Arun, Roshan Ayyalasomayajula, William Hunter, Dinesh Bharadia
RAL 2022
Presented at ICRA 2022
[Paper]-[4-min video ] -
ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM
Aditya Arun, William Hunter, Roshan Ayyalasomayajula, Dinesh Bharadia
Arxiv 2022
[Paper] -
WiROS: WiFi sensing toolbox for robotics
Aditya Arun*, William Hunter*, Dinesh Bharadia
*Equal contribution
Arxiv 2023
[Paper]-[Code]-[Demo]
cm-Accurate, Real time, and scalable UWB based Indoor 3D Localization
Since UWB has been developed as localization specific protocol, there has been a need for infrastructure based, low-power and real-time indoor localization while providing cm-Accurate 3D UWB tag locations. We solve these problems by novel hardware, firmware and algorithm designs. We are designing our custom UWB anchor hardware and firmware that enables accurate 3D AoA measurement to get cm-accurate 3D locations using our novel algorithms.
- ULoc: Low-Power, Scalable and cm-Accurate UWB-Tag Localization and Tracking for Indoor Applications
Minghui Zhao, Tyler Chang, Aditya Arun , Roshan Ayyalasomayajula, Chi Zhang, Dinesh Bharadia
IMWUT, 2021
Presented at Ubicomp, 2021
[Paper]-[20-min video]-[6-min video]-[Demo 1]-[Demo 2]-[Source] - Demo: Real-Time Low-Latency Tracking for UWB tags. In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications, and Services
Aditya Arun, Tyler Chang, Yizheng Yu, Roshan Ayyalasomayajula, Dinesh Bharadia
Mobisys, 2022
Presented at Mobisys, 2022
[Paper]-[Video]
Physical-layer Privacy from WiFi localization
RF sensor data can pose serious security and privacy risks if compromised by attackers in XR and autonomous applications. To address this issue, we developed MIRAGE, the first RF-privacy tool that allows users to distort RF-sensor data without leaving any traces of distortion.
- Users are Closer than they Appear: Protecting User Location from WiFi APs
Roshan Ayyalasomayajula, Aditya Arun , Wei Sun, Dinesh Bharadia
HotMobile, 2023
Presented at Hotmobile, 2023
[Paper]-[Poster]
Deep-Learning and Context assisted Indoor Wireless localization
There has been a lot of work in Indoor WiFi localization in the past decade, with none of them being deployed in real-world. With this project we intend to bridge the gap between the real-world maps and the WiFi maps and enable deep-learning based solution by allowing large scale data-collection.
- Deep Learning based Wireless Localization for Indoor Navigation
Roshan Ayyalasomayajula, Aditya Arun , Chenfeng Wu, Sanatan Sharma, Abhishek Sethi Deepak Vasisht, Dinesh Bharadia
ACM Mobicom, 2020
[paper]-[ppt]-[Video]-[codes]-[Datasets] - OpenSourcing: Wireless Indoor Localization Datasets (WILD)
Roshan Ayyalasomayajula, Aditya Arun , Chenfeng Wu, Dinesh Bharadia
[Datasets] - WAIP: Wireless AI Perception
Workshop at CVPR, 2022
[Website]-[competition]
Accurate Wi-Fi Anchor Localization
With the advent of CSI based WiFi localization, the indoor localization paradigm has come down to decimeter level accurate location estimates. While this remains the case, accuracy of anchor location, hitherto unsolved but an important parameter, is analyzed and solved in this research work.
BLE-based people interaction monitoring
- Poster Abstract: BluBLE, Space-time social distancing to monitor the spread of COVID-19
Aditya Arun, Agrim Gupta, Shivani Bhatka, Saikiran Komatineni, Dinesh Bharadia
Sensys, 2020
[Paper]