A hierarchical octree-transformer framework for LiDAR place recognition, multi-scale re-ranking, and 6-DoF localization in forests reports 90.7% Recall@1 on CS-Wild-Places and under 2 m / 5° error on 97.2% of registrations with nearly 100x runtime gains over RANSAC.
Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training,
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HOTFLoc++: End-to-End Hierarchical LiDAR Place Recognition, Re-Ranking, and 6-DoF Metric Localisation in Forests
A hierarchical octree-transformer framework for LiDAR place recognition, multi-scale re-ranking, and 6-DoF localization in forests reports 90.7% Recall@1 on CS-Wild-Places and under 2 m / 5° error on 97.2% of registrations with nearly 100x runtime gains over RANSAC.