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.
CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.