7 citations · 11 across the 11 of their papers we have counts for
11 papers
LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization
Kevin Christiansen Marsim, Minho Oh, Byeongho Yu +4
Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and…
DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains
Seunghyun Lee, I Made Aswin Nahrendra, Dongkyu Lee +3
Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or join…
TRG-planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation
Dongkyu Lee, I Made Aswin Nahrendra, Minho Oh +2
Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular…
TRIP: Terrain Traversability Mapping With Risk-Aware Prediction for Enhanced Online Quadrupedal Robot Navigation
Minho Oh, Byeongho Yu, I Made Aswin Nahrendra +8
Accurate traversability estimation using an online dense terrain map is crucial for safe navigation in challenging environments like construction and disaster areas. However, trave…
TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene Mapping
Seoyeon Jang, Minho Oh, Byeongho Yu +4
Safe navigation with simultaneous localization and mapping (SLAM) for autonomous robots is crucial in challenging environments. To achieve this goal, detecting moving objects in th…
B-TMS: Bayesian Traversable Terrain Modeling and Segmentation Across 3D LiDAR Scans and Maps for Enhanced Off-Road Navigation
Minho Oh, Gunhee Shin, Seoyeon Jang +7
Recognizing traversable terrain from 3D point cloud data is critical, as it directly impacts the performance of autonomous navigation in off-road environments. However, existing se…
Robust Recovery Motion Control for Quadrupedal Robots via Learned Terrain Imagination
I Made Aswin Nahrendra, Minho Oh, Byeongho Yu +2
Quadrupedal robots have emerged as a cutting-edge platform for assisting humans, finding applications in tasks related to inspection and exploration in remote areas. Nevertheless,…
DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning
I Made Aswin Nahrendra, Byeongho Yu, Hyun Myung
Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant…
Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Drones
I Made Aswin Nahrendra, Christian Tirtawardhana, Byeongho Yu +2
Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many stu…
STEP: State Estimator for Legged Robots Using a Preintegrated foot Velocity Factor
Yeeun Kim, Byeongho Yu, Eungchang Mason Lee +3
We propose a novel state estimator for legged robots, STEP, achieved through a novel preintegrated foot velocity factor. In the preintegrated foot velocity factor, the usual non-sl…
WALK-VIO: Walking-motion-Adaptive Leg Kinematic Constraint Visual-Inertial Odometry for Quadruped Robots
Hyunjun Lim, Byeongho Yu, Yeeun Kim +4
In this paper, WALK-VIO, a novel visual-inertial odometry (VIO) with walking-motion-adaptive leg kinematic constraints that change with body motion for localization of quadruped ro…