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About

Ehsan Zobeidi is a robotics researcher advancing multiagent perception and mapping under real-world constraints. His work centers on probabilistic metric-semantic mapping, where mobile robots—operating individually or in teams—construct dense, online 3D maps that encode both geometry and semantic labels (e.g., chair, table, wall). A key innovation is his use of sparse Gaussian process regression to cast mapping as a Bayesian inference problem, enabling robots to maintain full distributional uncertainty over surfaces and categories from streaming RGB-D data. This approach yields maps that are both incrementally updated and information-rich, supporting robust decision-making in unknown environments. His most-cited paper, “Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression” (2022, 18 citations), extends this framework to robot teams, while his earlier 2020 work (12 citations) laid the foundation for single-robot mapping. More recently, Zobeidi has tackled the challenge of distributed mapping under time-varying communication, a critical step toward cooperative autonomy in the field. With contributions that bridge theoretical rigor and practical deployment, his research is shaping the next generation of autonomous systems that can perceive, learn, and collaborate in complex, unstructured spaces.