Researchers at Guizhou University have developed SAGE-DQN SLAM, a deep reinforcement learning system that uses semantic scene understanding and dual temporal experts to automatically tune visual SLAM parameters, cutting trajectory errors in over 72 percent of benchmark sequences.
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