Research talk by Aditya Arun (University of California, San Diego)
Title: Leveraging WiFi for Robust and Resource-Efficient SLAM
Lecturer: Aditya Arun (UCSD)
Abstract:
Indoor robots can increasingly deliver value in diverse industry segments, including logistics, security, and construction. This demand has consequently increased the importance of robust simultaneous localization and mapping (SLAM) algorithms for indoor robots. This robustness is typically provided by fusing information from visual sensors (LiDARs or cameras) with proprioceptive sensors (odometers or IMUs). However, visual sensors can be sensitive to perceptual aliasing, visually dynamic environments, and changing lighting conditions, resulting in failures in SLAM predictions. In this talk, I will present WiFi radios as camera-like sensors capable of circumventing these issues and develop a real-time SLAM system to provide drift-free trajectory updates. We build our system with off-the-shelf components and evaluate it over four large-scale datasets in three indoor environments, traveling a cumulative distance of over 1500 m. Through these extensive evaluations, we find employing WiFi-based sensing provides a 6x improvement over purely relying on odometry. Additionally, we see a 4x reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems.
Speaker Bio:
Aditya Arun is a final-year Ph.D. student at the University of California, San Diego, advised by Dinesh Bharadia. He is part of the WCSNG group, the Center for Wireless Communications (CWC), and the Contextual Robotics Institute (CRI). His larger research vision is to incorporate WiFi and other wireless technologies as sensing modalities to improve the world of robotics and enable robotics to solve real-world problems. His research interests span wireless sensing, robotics, signal processing, and networking. Previously, he completed his B.S. from the University of California, Berkeley.
Web:
Time and Location:
Monday, June 3rd, 2024
4:00pm – 5:00pm – Eng. Bldg. 2, Room 112C1
Zoom: Join the lecture