4 citations · 6 across the 5 of their papers we have counts for
5 papers
Surfel-LIO: Fast LiDAR-Inertial Odometry with Pre-computed Surfels and Hierarchical Z-order Voxel Hashing
Seungwon Choi, Dong-Gyu Park, Seo-Yeon Hwang +1
LiDAR-inertial odometry (LIO) is an active research area, as it enables accurate real-time state estimation in GPS-denied environments. Recent advances in map data structures and s…
Calibrated Keyframe-Segment Selection for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments
Jaehyun Kim, Seungwon Choi, Wonseok Kang +1
False LiDAR loop closures can distort pose graphs in repetitive environments, where one query may match several distant places. Genuine revisits, unlike many perceptual aliases, te…
Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
Seungwon Choi, Donggyu Park, Seo-Yeon Hwang +1
A fundamental challenge in robust visual-inertial odometry (VIO) is to dynamically assess the reliability of sensor measurements. This assessment is crucial for properly weighting…
Efficient Graduated Non-Convexity for Pose Graph Optimization
Wonseok Kang, Jaehyun Kim, Jiseong Chung +2
We propose a novel approach to Graduated Non-Convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backend…
Adaptive Graduated Non-Convexity for Pose Graph Optimization
Seungwon Choi, Wonseok Kang, Jiseong Chung +2
We present a novel approach to robust pose graph optimization based on Graduated Non-Convexity (GNC). Unlike traditional GNC-based methods, the proposed approach employs an adaptiv…