DORF: A Dynamic Object Removal Framework for Robust Static LiDAR Mapping in Urban Environments-Reference-Cited by-同舟云学术

DORF: A Dynamic Object Removal Framework for Robust Static LiDAR Mapping in Urban Environments

Published:2023-12 Issue:12 Volume:8 Page:7922-7929
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Chen Zhiming¹^ORCID,Zhang Kun¹^ORCID,Chen Hua²^ORCID,Wang Michael Yu³^ORCID,Zhang Wei²^ORCID,Yu Hongyu¹^ORCID

Affiliation:

1. Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong, SAR, China

2. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China

3. Department of Mechanical Engineering, Monash University, Clayton, VIC, Australia

Funder

Innovation and Technology Commission

HKSAR

Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone

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/ielx7/7083369/10287676/10274835.pdf?arnumber=10274835

Reference33 articles.

1. Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data

2. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

3. Robust Method for Removing Dynamic Objects from Point Clouds

4. OctoMap : A probabilistic, flexible, and compact 3D map representation for robotic systems;wurm;Proc ICRA Workshop Best Pract 3D Percep Model Mobile Manipulation,0

5. Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

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2. FADOR: Fast and Accurate Dynamic Object Removal for Indoor Scenes;IEEE Transactions on Instrumentation and Measurement;2024

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