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cs.RO2026
OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching
Haedam Oh, Yifu Tao, Nived Chebrolu +1
Map representations which are consistent across repeated visits to a real-world semi-static environment are very useful for long-term robotic inspection. In such settings, the scen…
cs.RO2026
TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory
Minwoo Jung, Dongjae Lee, Nived Chebrolu +3
Reliable localization is essential for sustainable forest management, as it allows robots to revisit and monitor the status of individual trees over long periods. In modern forestr…
cs.RO2026
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
Minwoo Jung, Nived Chebrolu, Lucas Carvalho de Lima +3
Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions…