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About

Dorota Iwaszczuk is a researcher whose work sits at the intersection of 3D mapping, indoor navigation, and sensor fusion — areas with growing importance in robotics, autonomous systems, and augmented reality. Her most recognized contribution, "DEEPLIO" (2021, 15 citations), advances the state of the art in LiDAR-inertial odometry by applying deep learning to fuse LiDAR and IMU sensor data, offering more robust position and orientation estimation than classical approaches for autonomous driving and mobile robotics applications. Complementing this, her work on octree-based real-time 3D indoor mapping using RGB-D video data (2023, 5 citations) demonstrates a practical commitment to making high-quality spatial reconstruction accessible through low-cost depth cameras. Her research extends into the semantic dimension of indoor environments, with recent work on navigation network models built from 3D semantic point clouds (2025) pushing the frontier of intelligent path planning. Her earlier investigation into quantifying the quality of indoor maps (2019) reflects a rigorous methodological foundation underpinning all her contributions. Collectively, Iwaszczuk's research addresses the full pipeline from raw sensor data to reliable, semantically enriched spatial representations essential for next-generation indoor navigation systems.