Yeganeh Bahoo
Papers
1
Total Citations
2
H-Index
1
About
Yeganeh Bahoo is a researcher at the forefront of indoor robotics and sensor fusion, with a primary focus on leveraging ubiquitous WiFi signals for autonomous navigation. Her most notable contribution is the pioneering work "Structure from WiFi (SfW): RSSI-Based Geometric Mapping of Indoor Environments" (2024), which introduces a novel approach to Simultaneous Localization and Mapping (SLAM) by using received signal strength indicator (RSSI) data to construct geometric maps of indoor spaces. This work addresses a critical challenge in robotics: enabling reliable navigation in environments where traditional visual or LiDAR-based systems may fail, such as in poor lighting or cluttered settings. By transforming passive WiFi signals into actionable spatial information, Bahoo’s research has opened new pathways for cost-effective, infrastructure-free mapping. With 2 citations already in its early publication, this work is gaining traction for its practical implications in smart buildings, search-and-rescue, and service robotics. Bahoo’s contributions exemplify how everyday technology can be repurposed to solve complex engineering problems, making her a rising voice in the integration of wireless communications and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1