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


  • 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.


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.

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.

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.

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

  • OpenSourcing: Wireless Indoor Localization Datasets (WILD)
    Roshan Ayyalasomayajula, Aditya Arun , Chenfeng Wu, Dinesh Bharadia

  • WAIP: Wireless AI Perception
    Workshop at CVPR, 2022

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.

  • LocAP: Accurate Localization of Existing WiFi Infrastructure
    Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Shrivatsan Rajagopalan, Shreya Ganesaraman, Aravind Seetharaman, Dinesh Bharadia
    USENIX NSDI, 2020

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