Sahar Salimpour Kasebi

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About

Sahar Salimpour Kasebi’s research lies at the intersection of biologically inspired robotics, simultaneous localization and mapping (SLAM), and hybrid navigation systems. Her most cited work, “Hybrid navigation based on GPS data and SIFT-based place recognition using Biologically-inspired SLAM” (2021), introduces an enhanced RatSLAM framework—a computational model mimicking the rodent hippocampus—integrated with Scale Invariant Feature Transform (SIFT) for robust place recognition in indoor environments. By fusing GPS data with visual cues, her approach significantly improves localization accuracy and map consistency, addressing key challenges in autonomous navigation where GPS alone fails. Though early in her career, this work has garnered 4 citations, signaling growing interest in bio-inspired solutions for robotics. Kasebi’s contributions are notable for bridging computational neuroscience and practical robotics, offering a pathway to more resilient navigation systems. Her research holds promise for applications in autonomous vehicles, service robots, and assistive technologies, making her a rising voice in the field of intelligent, nature-inspired robotic perception.