Sahar Salimpour
Papers
9
Total Citations
87
H-Index
6
About
Sahar Salimpour is an emerging robotics and autonomous systems researcher whose work spans multi-robot coordination, robotic perception, and decentralized security frameworks. Her research makes significant contributions at the intersection of deep learning, computer vision, and multi-robot systems, with a particular focus on enabling robust autonomy in complex, real-world environments. Among her most impactful contributions is her pioneering work on Byzantine agent detection in multi-robot systems using IOTA smart contracts and distributed ledger technologies, which has garnered nearly 30 citations across related publications. She has also advanced robotic perception through her investigations into deep learning-based detection and segmentation using LiDAR-as-camera sensors, and developed self-calibrating anomaly detection algorithms for autonomous inspection robots, reflecting a strong thread of safety and reliability throughout her research agenda. Salimpour has further pushed the boundaries of infrastructure-free localization, contributing benchmark datasets and novel anomaly-resilient methods for Ultra-Wideband-based multi-robot relative positioning. Her recent work on sim-to-real transfer using NVIDIA Isaac Sim and ROS 2 underscores her commitment to bridging theoretical advances with practical deployment. With a growing citation record exceeding 85 citations, she is establishing herself as a promising voice in autonomous robotics research.
Research Focus
Key Achievements
Top Papers
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