6 papers · 1 filter
RadLoc: Radar-based 3-DoF Global Localization via Fast, Robust, and Lightweight Spatial Descriptor Across Diverse Environmental Scenarios
Hogyun Kim, Jiwon Choi, Jungwoo Lee +1
While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies have focused on individual comp…
KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference
Jiwon Choi, Hogyun Kim, Geonmo Yang +2
Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing modalities in robotic systems. R…
Commerge: Communication-Efficient, Robust, and Fast LiDAR Map Merging Framework for Multi-Robot Coordination in Resource-Constrained Scenarios
Hogyun Kim, Jiwon Choi, Juwon Kim +4
By maintaining global consistency across robot teams, multi-robot LiDAR map merging enables faster exploration and efficient area coverage. However, map merging requires exchanging…
SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
Hogyun Kim, Jiwon Choi, Juwon Kim +4
Distributed LiDAR SLAM is crucial for achieving efficient robot autonomy and improving the scalability of mapping. However, two issues need to be considered when applying it in fie…
PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal
Jiwon Choi, Dongjin Cho, Gihyeon Lee +4
Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging cond…
Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor for FOV-constrained LiDAR Place Recognition
Hogyun Kim, Jiwon Choi, Taehu Sim +2
We often encounter limited FOV situations due to various factors such as sensor fusion or sensor mount in real-world robot navigation. However, the limited FOV interrupts the gener…