Soohyun Shin

About

Soohyun Shin is a rising researcher in the field of autonomous robotics and 3D perception, with a primary focus on LiDAR-based odometry and deep learning for spatial-temporal modeling. Their most notable contribution is the development of LoRCoN-LO (Long-term Recurrent Convolutional Network-based LiDAR Odometry), a novel approach that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) layers to simultaneously process spatial and temporal information for more accurate and robust pose estimation. This work, published in 2023, has already garnered 3 citations, signaling early impact in the community. Shin’s research addresses a critical challenge in autonomous navigation—enabling vehicles to maintain precise localization over long trajectories without drift. By leveraging the LRCN structure, their method outperforms traditional geometric approaches in complex environments. As a forward-thinking engineer, Shin’s contributions are paving the way for more reliable, learning-based odometry systems, making their work essential reading for students and researchers in robotics, computer vision, and deep learning.

Research Focus

Key Achievements

Top Papers

1. 1
   LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry

   3 citations · 2023

Key Collaborators

Contact & Links