Evaluates three methods for trajectory accuracy in multi-session ground texture SLAM under low-dynamic changes, identifies Kullback-Leibler Divergence as most effective, and introduces a new evaluation dataset.
G2P-SLAM: Generalized RGB-D SLAM framework for mobile robots in low-dynamic environ- ments,
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Multi-Session Ground Texture SLAM in Low-Dynamic Environments
Evaluates three methods for trajectory accuracy in multi-session ground texture SLAM under low-dynamic changes, identifies Kullback-Leibler Divergence as most effective, and introduces a new evaluation dataset.