Paired-CSLiDAR benchmark and Residual-Guided Stratified Registration achieve 86% success at 0.75 m RMSE on 9,012 cross-source pairs by height-stratified ICP and confidence-gated selection.
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
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Factor graph optimization problems can be lifted via Shor's relaxation and Burer-Monteiro factorization to produce certifiable estimators that preserve the original factor graph connectivity and can be implemented with existing libraries.
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.
ADM-Fusion proposes an end-to-end adaptive multi-sensor fusion network using mixture-of-experts routing and cross-task attention for robust ego-motion estimation, trained on simulation then fine-tuned on real data.
Jiao architecture reduces cycle-period jitter by 84.5% and eliminates large timing excursions in mixed-criticality robotic systems via Safe IO Cell, Parameter Synchronization Service, and Safety Communication Layer.
citing papers explorer
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Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement
Paired-CSLiDAR benchmark and Residual-Guided Stratified Registration achieve 86% success at 0.75 m RMSE on 9,012 cross-source pairs by height-stratified ICP and confidence-gated selection.
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Certifiable Factor Graph Optimization
Factor graph optimization problems can be lifted via Shor's relaxation and Burer-Monteiro factorization to produce certifiable estimators that preserve the original factor graph connectivity and can be implemented with existing libraries.
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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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ADM-Fusion: Adaptive Deep Multi-Sensor Fusion for Robust Ego-Motion Estimation in Diverse Conditions
ADM-Fusion proposes an end-to-end adaptive multi-sensor fusion network using mixture-of-experts routing and cross-task attention for robust ego-motion estimation, trained on simulation then fine-tuned on real data.
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Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics
Jiao architecture reduces cycle-period jitter by 84.5% and eliminates large timing excursions in mixed-criticality robotic systems via Safe IO Cell, Parameter Synchronization Service, and Safety Communication Layer.