How to Install and Uninstall libnanoflann-dev Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: May 18,2024

1. Install "libnanoflann-dev" package

Please follow the step by step instructions below to install libnanoflann-dev on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libnanoflann-dev
            
                    
    
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2. Uninstall "libnanoflann-dev" package

This tutorial shows how to uninstall libnanoflann-dev on Ubuntu 21.04 (Hirsute Hippo):


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

Package: libnanoflann-dev
Architecture: all
Version: 1.3.2+ds-1
Multi-Arch: foreign
Priority: optional
Section: universe/libdevel
Source: nanoflann
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1764
Filename: pool/universe/n/nanoflann/libnanoflann-dev_1.3.2+ds-1_all.deb
Size: 167728
MD5sum: 8a8df6284f712927d3b7dd04f614ed79
SHA1: 13508b8fe6d1c9bccd53175c28fe6b58c5ea971d
SHA256: 992c6a036e2de8a903b8af1e45af50f54f138207e45064b05e9ae990a76c3aca
SHA512: 64088c90d293a62166f1342a80221a405fff8e1363c46706a67c4212b70ba536f81b2bdfbd880fe69cd75902b98215399576f36b565c5b365bc7a39af2ce4221
Homepage: https://github.com/jlblancoc/nanoflann
Description-en: C++11 header-only library for Nearest Neighbor Search with KD-Trees
Nanoflann is a fork of the FLANN library. Originally born as a child project
of the Mobile Robot Programming Toolkit, nanoflann is now available as
stand-alone library.
.
It trades some of the flexibility of FLANN for raw execution speed and is much
more memory efficient, as it avoids unnecessary copies of point cloud data.
Description-md5: c314502787f875d29905ec23097f498e