How to Install and Uninstall libpcl-doc Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 07,2024

1. Install "libpcl-doc" package

Please follow the guidelines below to install libpcl-doc on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libpcl-doc
            
                    
    
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2. Uninstall "libpcl-doc" package

Learn how to uninstall libpcl-doc on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                libpcl-doc
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libpcl-doc package on Ubuntu 21.04 (Hirsute Hippo)

Package: libpcl-doc
Architecture: all
Version: 1.11.1+dfsg-1build1
Multi-Arch: foreign
Priority: extra
Section: universe/doc
Source: pcl
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 210974
Depends: libjs-jquery
Filename: pool/universe/p/pcl/libpcl-doc_1.11.1+dfsg-1build1_all.deb
Size: 14533360
MD5sum: 66ff2bdfec664dcc7c643ae43664aa49
SHA1: 085746d7dc2718619ee5bdd56c41a4696010c74e
SHA256: 713e2c1a0fa963193724493802c9ae786890f166af537ba8b124480e2f9a5052
SHA512: 45258d968527249d1327b42194aa14f33d65fe4d44ebecf31f16921d5b4fb84b6b7d63c914dfc49098947ea973a67e6936f0dffdd1151b277ff820af0c25a199
Homepage: http://www.pointclouds.org/
Description-en: Point Cloud Library - documentation
The Point Cloud Library (PCL) is a standalone, large scale, open
project for 2D/3D image and point cloud processing.
.
The PCL framework contains numerous state-of-the art algorithms
including filtering, feature estimation, surface reconstruction,
registration, model fitting and segmentation.
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This package contains the Doxygen generated documentation.
Description-md5: 2eac989d9e8426e817a291a4d72d8b4d