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UNIST Develops Ultrafast Spatial AI That Cuts Mapping Errors 45.9%
Researchers at Ulsan National Institute of Science and Technology have created UniSim‑SLAM, an AI‑driven spatial recognition system that builds precise three‑dimensional maps from camera images while tracking moving objects in real time. The method merges rapid two‑frame pose estimation with detailed sub‑map analysis, using Sim(3) factor‑graph optimization to align scale, rotation and position across both data streams. Benchmarked on the 7‑Scenes indoor dataset, UniSim‑SLAM achieved an average path error of 2.0 cm, cutting errors by up to 45.9 % [...]