9 papers
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…
Self-supervised Geometry Reasoning for LiDAR Simultaneous Localization and Mapping
Jiwoo Kim, Jinwoo Lee, Woojae Shin +2
LiDAR simultaneous localization and mapping (SLAM) relies on local geometric quantities such as covariances, correspondences, and surface structures. However, most existing pipelin…
Streaming Dense Voxel Representations for 3D Occupancy Prediction
Seokha Moon, Janghyun Baek, Yujin Jeong +5
In this paper, we explore dense voxel streaming for accurate and efficient 3D occupancy prediction. While dense voxel representations offer fine-grained spatial details and streami…
Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice
Jinwoo Lee, Jiwoo Kim, Woojae Shin +2
Point clouds are a fundamental representation for robotic perception tasks such as localization, mapping, and object pose estimation. However, LiDAR-acquired point clouds are inher…
MR.ScaleMaster: Scale-Consistent Collaborative Mapping from Crowd-Sourced Monocular Videos
Hyoseok Ju, Giseop Kim
Crowd-sourced cooperative mapping from monocular cameras promises scalable 3D reconstruction without specialized sensors, yet remains hindered by two scale-specific failure modes:…
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…