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.
Switchable constraints vs. max-mixture models vs. RRR – a comparison of three approaches to robust pose graph SLAM
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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.