TY - GEN
T1 - Poster
T2 - 24th ACM Annual International Conference on Mobile Systems, Applications and Services, MobiSys Companion 2026
AU - Sirigadi, Sai Bharadwaj
AU - Ayyalasomayajula, Roshan
N1 - Publisher Copyright:
© 2026 Copyright held by the owner/author(s).
PY - 2026/6/20
Y1 - 2026/6/20
N2 - Resource constrained robots have immense potential in autonomous exploration in the wild for dull, dirty, and dangerous tasks. To enable such autonomous robotic deployments, the primary limitation is reliability on minimalist sensors. So, we aim to develop autonomous robots that repurpose Wi-Fi chipsets as localization sensors and fuse their measurements with IMU data for navigation. While it would have been an easier task if the robots had multiple antennas, to perform single antenna based navigation, we have to rely on TDoA measurements. So, in this project, we aim to, for the first time, develop robust and tightly coupled TDoA and IMU fusion for mobile robot localization. We formulate the localization problem as a nonlinear optimization over a factor graph, where TDoA measurements are incorporated as factors that constrain the solution space. Simulation results demonstrate consistent convergence and accurate state estimation under the proposed framework.
AB - Resource constrained robots have immense potential in autonomous exploration in the wild for dull, dirty, and dangerous tasks. To enable such autonomous robotic deployments, the primary limitation is reliability on minimalist sensors. So, we aim to develop autonomous robots that repurpose Wi-Fi chipsets as localization sensors and fuse their measurements with IMU data for navigation. While it would have been an easier task if the robots had multiple antennas, to perform single antenna based navigation, we have to rely on TDoA measurements. So, in this project, we aim to, for the first time, develop robust and tightly coupled TDoA and IMU fusion for mobile robot localization. We formulate the localization problem as a nonlinear optimization over a factor graph, where TDoA measurements are incorporated as factors that constrain the solution space. Simulation results demonstrate consistent convergence and accurate state estimation under the proposed framework.
KW - factor graph optimization
KW - inertial measurement unit (IMU)
KW - robot localization
KW - sensor fusion
KW - time difference of arrival (TDoA)
KW - wireless localization
UR - https://www.scopus.com/pages/publications/105044891049
U2 - 10.1145/3812835.3814872
DO - 10.1145/3812835.3814872
M3 - Conference contribution
AN - SCOPUS:105044891049
T3 - MobiSys Companion 2026 - Companion Proceedings of the 24th ACM Annual International Conference on Mobile Systems, Applications and Services, Part of MobiSys 2026
SP - 45
EP - 46
BT - MobiSys Companion 2026 - Companion Proceedings of the 24th ACM Annual International Conference on Mobile Systems, Applications and Services, Part of MobiSys 2026
PB - Association for Computing Machinery, Inc
Y2 - 21 June 2026 through 25 June 2026
ER -