PROBE is a learning-free LiDAR place recognition descriptor using probabilistic Bernoulli occupancy in BEV with analytical translation marginalization via polar Jacobian, achieving top handcrafted accuracy on multi-session tasks across four datasets.
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A framework gives necessary and sufficient coupling conditions for cross-factor observability on product Lie groups plus exact decompositions of spatial versus temporal information in the error covariance.
UCATSC uses belief-state rollouts and predictive safety constraints to enable competitive, dilemma-zone-free traffic signal control under vision-based partial observability.
QCSA reduces inserted loop factors 3.8 times and raises precision from 0.542 to 0.717 on the SNULib dataset while lowering worst-case trajectory error compared with dense Top-1+G-ICP baselines.
A survey of the bidirectional relationship between SLAM techniques and wireless communications, covering concepts, methods, and open challenges in their integration.
citing papers explorer
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PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition
PROBE is a learning-free LiDAR place recognition descriptor using probabilistic Bernoulli occupancy in BEV with analytical translation marginalization via polar Jacobian, achieving top handcrafted accuracy on multi-session tasks across four datasets.
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Space-Time Diversity in Observability and Estimation on Product Lie Groups
A framework gives necessary and sufficient coupling conditions for cross-factor observability on product Lie groups plus exact decompositions of spatial versus temporal information in the error covariance.
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UCATSC: Uncertainty-Aware Constrained Traffic Signal Control Under Vision-Based Partial Observability
UCATSC uses belief-state rollouts and predictive safety constraints to enable competitive, dilemma-zone-free traffic signal control under vision-based partial observability.
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Query-Calibrated Segmental Admission for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments
QCSA reduces inserted loop factors 3.8 times and raises precision from 0.542 to 0.717 on the SNULib dataset while lowering worst-case trajectory error compared with dense Top-1+G-ICP baselines.
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When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey
A survey of the bidirectional relationship between SLAM techniques and wireless communications, covering concepts, methods, and open challenges in their integration.