Seokjoon Kim — accepted AI-conference papers

2025

Learning Fast, Tool-Aware Collision Avoidance for Collaborative Robots

Joonho Lee, Yunho Kim, Seokjoon Kim, Quan Nguyen, Young Jin Heo

RA-L 2025

Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly

Cited by 1 Source ↗Scholar ↗

2023

Fast and Versatile Feature-Based LiDAR Odometry via Efficient Local Quadratic Surface Approximation

Seungwon Choi, Hee-Won Chae, Yunsuk Jeung, Seokjoon Kim, Kyusung Cho, Tae-wan Kim

RA-L 2023

We present a fast and versatile feature-based LiDAR odometry method using local quadratic surface approximation and point-to-surface alignment. Unlike most feature-based methods, our approach approximates the local geometry of the LiDAR scan as a quadratic surface to mitigate performance degradation

Cited by 13 Source ↗Scholar ↗