Sunggoo Jung — accepted AI-conference papers

2023

Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs

Muhammad Fadhil Ginting, Sung-Kyun Kim, Oriana Peltzer, Joshua Ott, Sunggoo Jung, Mykel J. Kochenderfer +1

ICRA 2023poster

To achieve autonomy in unknown and unstruc-tured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is cru-cial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion po…

Cited by 8 Source ↗Scholar ↗

2022

Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data

John So, Amber Xie, Sunggoo Jung, Jeffrey Edlund, Rohan Thakker, Ali-akbar Agha-mohammadi +2

CoRL 2022poster

Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these components, we instead consider an end-to-end approach via reinforcement learning (RL). However, collecting exploration driving…

Cited by 11 Source ↗code ↗Scholar ↗

2018

Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning

Sunggoo Jung, Sunyou Hwang, Heemin Shin, David Hyunchul Shim

RA-L 2018

In autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image pr

Cited by 167 Source ↗Scholar ↗