David Skuddis

University of Stuttgart, Diehl (Germany)

Papers

Total Citations

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About

David Skuddis is an emerging researcher specializing in mobile robotics, autonomous systems, and 3D environmental perception. His work focuses on two critical challenges in robotics: simultaneous localization and mapping (SLAM) and LiDAR-based ground segmentation, both of which are foundational to enabling robots to navigate and understand complex real-world environments. His most notable contribution, "SLAM for Indoor Mapping of Wide Area Construction Environments" (2024), has already garnered 6 citations, demonstrating rapid uptake within the robotics community. This work addresses the particularly demanding problem of mapping large-scale, complex spaces such as factory halls — environments where traditional SLAM approaches often struggle. Complementing this, his 2022 paper on LiDAR ground segmentation tackles the equally challenging problem of robot perception in unstructured natural terrain like forests and meadows, where urban-focused algorithms frequently fail. Together, these contributions reflect Skuddis's commitment to pushing robotic perception beyond controlled or well-structured settings into messy, real-world applications. While still early in his research career, his focus on practically relevant, high-impact problems positions him as a promising voice in the fields of autonomous navigation and 3D point cloud processing.