End-to-End Visual Odometry with RNNs and Attention

arXiv:2609.26188 · cs.CV · Submitted 2026-08-10 · Read on arXiv

cs.CV

Submitted: 2026-08-10

Updated: 2026-08-10

Terminology

• Transformer — 17× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Long Short-Term Memory — 1× in this paper
• Optical Flow — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Simultaneous Localisation and Mapping — 1× in this paper
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• Video Odometry — 1× in this paper

Sources

• DOT: Dynamic Object Tracking for Visual SLAM
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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