A new framework solves the kidnapped robot problem by sampling sparse feasible hypotheses via constrained RRT and applying batched multi-stage inference with SMAD ordering and TAM alignment to achieve competitive success rate and efficiency on real robots.
Extracting Statistical Signatures of Geometry and Structure in 2D Occupancy Grid Maps for Global Localization,
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Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference
A new framework solves the kidnapped robot problem by sampling sparse feasible hypotheses via constrained RRT and applying batched multi-stage inference with SMAD ordering and TAM alignment to achieve competitive success rate and efficiency on real robots.