4 papers
LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding
Yumin Lee, Hyoseok Ju, Giseop Kim
Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either overwrite history to maintain…
Mag4D-SLAM Dataset: A Repeated-Traversal Multi-Modal 4D Geomagnetic Dataset for Localization and Mapping
Bibhutibhusan Nayak, Hyoseok Ju, Giseop Kim
Geomagnetic sensing offers an infrastructure-free, absolute orientation reference that is robust to GNSS denial and visual degradation, yet no large-scale outdoor robotics dataset…
MR.ScaleMaster: Scale-Consistent Collaborative Mapping from Crowd-Sourced Monocular Videos
Hyoseok Ju, Giseop Kim
Crowd-sourced cooperative mapping combines monocular sessions from different front-ends, each an independently reconstructed keyframe sequence. Each session has its own local coord…
Have We Mastered Scale in Deep Monocular Visual SLAM? The ScaleMaster Dataset and Benchmark
Hyoseok Ju, Bokeon Suh, Giseop Kim
Recent advances in deep monocular visual Simultaneous Localization and Mapping (SLAM) have achieved impressive accuracy and dense reconstruction capabilities, yet their robustness…