A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2representative citing papers
A resident underwater vehicle with docking station achieves 90% autonomous docking success and completes inspections in four minutes using fused acoustic and visual navigation at 90 m depth.
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Temporally Consistent Object 6D Pose Estimation for Robot Control
A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
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Docking and Persistent Operations for a Resident Underwater Vehicle
A resident underwater vehicle with docking station achieves 90% autonomous docking success and completes inspections in four minutes using fused acoustic and visual navigation at 90 m depth.