20 citations · 33 across the 6 of their papers we have counts for
6 papers
Staircase Localization for Autonomous Exploration in Urban Environments
Jinrae Kim, Sunggoo Jung, Sung-Kyun Kim +2
A staircase localization method is proposed for robots to explore urban environments autonomously. The proposed method employs a modular design in the form of a cascade pipeline co…
Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs
Muhammad Fadhil Ginting, Sung-Kyun Kim, Oriana Peltzer +4
To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and…
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
John So, Amber Xie, Sunggoo Jung +5
Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these compone…
Mapless Navigation: Learning UAVs Motion forExploration of Unknown Environments
Sunggoo Jung, David Hyunchul Shim
This study presents a new methodology for learning-based motion planning for autonomous exploration using aerial robots. Through the reinforcement learning method of learning throu…
Robust Collision-free Lightweight Aerial Autonomy for Unknown Area Exploration
Sunggoo Jung, Hanseob Lee, David Hyunchul Shim +1
Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This st…
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Ali Agha, Kyohei Otsu, Benjamin Morrell +69
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…