Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation-Reference-Cited by-同舟云学术

Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation

Published:2022-07 Issue:3 Volume:7 Page:6107-6114
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Chen Xieyuanli¹^ORCID,Mersch Benedikt¹^ORCID,Nunes Lucas¹^ORCID,Marcuzzi Rodrigo¹^ORCID,Vizzo Ignacio¹^ORCID,Behley Jens¹^ORCID,Stachniss Cyrill¹^ORCID

Affiliation:

1. University of Bonn, Bonn, Germany

Funder

Deutsche Forschungsgemeinschaft

Germany’s Excellence Strategy

EC through Horizon Europe

Chinese Scholarship Committee

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09756222.pdf?arnumber=9756222

Reference38 articles.

1. Learning an Overlap-based Observation Model for 3D LiDAR Localization

2. 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics

3. Density-Based Clustering Based on Hierarchical Density Estimates

4. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

5. Fast range image-based segmentation of sparse 3D laser scans for online operation

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