Saeed Gholami Shahbandi

Laboratoire Interuniversitaire des Systèmes Atmosphériques, Halmstad University

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About

Saeed Gholami Shahbandi’s research lies at the intersection of robotic perception, mapping, and semantic understanding, with a particular focus on enabling autonomous systems to interpret and navigate structured environments. His work bridges the gap between raw sensor data and high-level spatial reasoning, developing methods that allow robots to not only see but also understand the spaces they occupy. A key contribution is his semi-supervised approach to semantic labeling, which combines unsupervised place categorization with adaptive cell decomposition of occupancy maps, allowing human knowledge to be introduced efficiently. This work, along with his research on infrastructure mapping using Micro Aerial Vehicles (MAVs), demonstrates his commitment to creating robust, practical solutions for real-world robotic deployment. Shahbandi has also tackled the fundamental challenge of map alignment, proposing a region decomposition method to correctly align prior maps with sensor-built maps, a critical step for localization and navigation. While his citation counts reflect a focused, early-career impact, his contributions to object recognition using radial basis function neural networks with RGB-D cameras show his foundational work in perception for mobile robotics. His research continues to shape how robots build and use semantic maps in well-structured environments.