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.
Efficient active global localization for mobile robots operating in large and cooperative environments
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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.