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Ikhyeon Cho's Projects

dijkstra_planner icon dijkstra_planner

The global planner used in the paper 'ODS-Bot: Mobile robot navigation for outdoor delivery services' (IEEE Access, 2022)

dwa_planner icon dwa_planner

The local planner used in the paper 'ODS-Bot: Mobile Robot Navigation for Outdoor Delivery Services' (IEEE Access, 2022)

fake_simulation_ros icon fake_simulation_ros

A simple RViz simulation package providing 2D laser scan and occupancy map. For testing navigation planning algorithms

fast_lio icon fast_lio

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

grid_map icon grid_map

Universal grid map library for mobile robotic mapping

height_mapping icon height_mapping

Range sensor-based 2.5D gridded height mapping (Digital Elevation Model) for urban terrain navigation of mobile robots

isr_robot_ros icon isr_robot_ros

ROS driver implementation of ISR M-series mobile robots, used in Intelligent Systems & Robotics Lab (ISR).

lego-loam icon lego-loam

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

lesta icon lesta

Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach (RA-L 2024 & ICRA@40)

lio-sam icon lio-sam

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

nanoflann icon nanoflann

nanoflann: a C++11 header-only library for Nearest Neighbor (NN) search with KD-trees

ods-bot icon ods-bot

ODS-Bot: Mobile robot navigation for outdoor delivery services (IEEE Access, 2022)

pptk icon pptk

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

roadbev icon roadbev

Codes for RoadBEV: road surface reconstruction in Bird's Eye View

urban-terrain-dataset icon urban-terrain-dataset

The dataset for the paper 'Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach'

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