paratextJan 1, 2024GREEN OA

2024 IEEE International Conference on Robotics and Automation (ICRA)

MPMortimer, PeterHRHagmanns, RaphaelGRGranero Ramos, MiguelPJPetereit, JankoLTLüttel, Thorsten

Universität der Bundeswehr München · Karlsruhe Institute of Technology · +1 more institution

Indexed inarxivcrossrefdatacite

Abstract

This dataset consists of semantically segmented LiDAR point clouds of the GOOSE and GOOSE-Ex dataset. The original point clouds annotations segmented all points into 64 semantic classes, but for the GOOSE 3D Semantic Segmentation Challenge on CodaBench we consolidated the point cloud data into 8 superclasses (+ other class): category_name,label_key,hex other,0,#A9A9A9 artificial_structures,1,#DE88DE artificial_ground,2,#EBFF3B natural_ground,3,#A1887F obstacle,4,#FFC107 vehicle,5,#F44336 vegetation,6,#4CAF50 human,7,#8FB0FF sky,8,#2196F3 The dataset contains 13006 annotated point clouds in total, stored in the .label format as is done in the SemanticKITTI dataset. import numpy as np # reading a .label file…

Citation impact

311
total citations
FWCI
—
Percentile
—
References
0
Citations per year

Authors

5
  • MP
    Mortimer, PeterCorresponding

    Universität der Bundeswehr München

  • HR
    Hagmanns, Raphael

    Karlsruhe Institute of Technology

  • GR
    Granero Ramos, Miguel

    Fraunhofer Institute of Optronics, System Technologies and Image Exploitation

  • PJ
    Petereit, Janko

    Fraunhofer Institute of Optronics, System Technologies and Image Exploitation

  • LT
    Lüttel, Thorsten

    Universität der Bundeswehr München

Topics & keywords

Topics
Keywords
  • Automation
  • Robotics
  • Artificial intelligence
  • Computer science
  • Engineering
  • Robot
  • Mechanical engineering
No related works found for this paper.

Funding