Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Physics-informed machine learning identifies a sparse control-affine model that is embedded in an adaptive tube MPC scheme for aerial vehicles, with stability proofs and demonstrated reductions in computation alongside improved tracking over baselines.
Cortex 2.0 introduces world-model-based planning that generates and scores future trajectories to outperform reactive vision-language-action baselines on industrial robotic tasks including pick-and-place, sorting, and unpacking.
citing papers explorer
-
Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
-
Physics-informed sparse identification-based tube model predictive control for aerial vehicles
Physics-informed machine learning identifies a sparse control-affine model that is embedded in an adaptive tube MPC scheme for aerial vehicles, with stability proofs and demonstrated reductions in computation alongside improved tracking over baselines.
-
Cortex 2.0: Grounding World Models in Real-World Industrial Deployment
Cortex 2.0 introduces world-model-based planning that generates and scores future trajectories to outperform reactive vision-language-action baselines on industrial robotic tasks including pick-and-place, sorting, and unpacking.