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Found 2 papers, 1 papers with code
Self-Improving SLAM in Dynamic Environments: Learning When to Mask
Thus, we propose a novel SLAM that learns when masking objects improves its performance in dynamic scenarios.
Learning to Segment Dynamic Objects using SLAM Outliers
Our dataset includes consensus inversions, i. e., situations where the SLAM uses more features on dynamic objects that on the static background.
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