Library/TRAVEL
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url-kaist/TRAVEL

TRAVEL

Traversable ground and above-ground object segmentation using graph representation of 3D LiDAR scans

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url-kaist

url-kaist

url-kaist • individual

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322

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Forks

33

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0/100

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Created

Feb 24, 2022

Project creation date

README Summary

TRAVEL is a method for segmenting traversable ground and above-ground objects from 3D LiDAR point clouds using graph-based representation. The approach creates a graph structure from LiDAR data to effectively separate ground surfaces that vehicles can traverse from obstacles and objects above ground. This enables robust terrain analysis for autonomous navigation systems.

AI Dev Skills

Unmapped

3D Point Cloud ProcessingGraph-based Machine LearningLiDAR Data AnalysisGeometric SegmentationSpatial Graph Neural Networks3D Scene UnderstandingTraversability Analysis

Tags

3D Point Cloud ProcessingGraph-based Machine LearningLiDAR Data AnalysisGeometric SegmentationSpatial Graph Neural Networks3D Scene UnderstandingTraversability AnalysisOn-device AI3D Environment Mapping3DTerrain Analysis for Off-road VehiclesRobot Path PlanningRoboticsAutonomous SystemsAutonomous Ground Vehicle NavigationObstacle Detection and AvoidanceConstruction3D Computer VisionAutonomous VehiclesAgricultureLiDAR Point CloudsGraph Neural NetworksEdge ComputingReal-time ProcessingMiningC++

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Recent Activity

Updated 1 years ago

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research
Quality
medium
Maturity
research

Categories

Computer VisionPrimaryRoboticsAI AgentsInference & ServingDev Tools & AutomationEdge & Mobile AIOther AI / ML

PM Skills

Developer Platform

Languages

C++100.0%

Timeline

Project created
Feb 24, 2022
Forked
Mar 13, 2026
Your last push
1 years ago
Upstream last push
1 years ago
Tracked since
Dec 16, 2024

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