A multi-hypothesis global relocalization framework uses RRT under traversability constraints for sparse feasible pose sampling, SMAD for early candidate ordering, and TAM for orientation and final evaluation to solve the kidnapped robot problem efficiently from a single non-panoramic LiDAR scan.
Active global localization based on localizability for mobile robots,
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Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference
A multi-hypothesis global relocalization framework uses RRT under traversability constraints for sparse feasible pose sampling, SMAD for early candidate ordering, and TAM for orientation and final evaluation to solve the kidnapped robot problem efficiently from a single non-panoramic LiDAR scan.