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Discrete-token adaptation of frozen MLLMs enables effective robot navigation with significantly less training data than conventional approaches.
SSD transforms radar place recognition by leveraging spatial alignment to overcome the limitations of sparse sensor data, leading to unprecedented accuracy in challenging conditions.
Depth-aware distillation boosts visual place recognition in forests, revealing that geometric cues can dramatically enhance model robustness against appearance variations.
