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gtsam package

gtsam 4.2.0+dfsg-1build1 source package in Ubuntu

Changelog

gtsam (4.2.0+dfsg-1build1) noble; urgency=medium

  * No-change rebuild with Python 3.12 as default

 -- Graham Inggs <email address hidden>  Fri, 19 Jan 2024 19:39:30 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Noble
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Noble release universe misc

Downloads

File Size SHA-256 Checksum
gtsam_4.2.0+dfsg.orig.tar.xz 10.9 MiB 8383cc1fe7ea82549c9cd6c0cead48712d3348f4193c0c16e776e57401343277
gtsam_4.2.0+dfsg-1build1.debian.tar.xz 20.5 KiB 8d7b0af256adf81880d95fda3100bab1b94b3b6e9bd590baa0c09429712df126
gtsam_4.2.0+dfsg-1build1.dsc 2.5 KiB ca8ab72d74e3508191124f347d2df86a1f748f91568cfbf63b3985e9dd430bf4

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Binary packages built by this source

libgtsam-dev: Factor graphs for sensor fusion in robotics

 GTSAM is a C++ library that implements sensor fusion for robotics and computer
 vision applications, including SLAM (Simultaneous Localization and Mapping), VO
 (Visual Odometry), and SFM (Structure from Motion). It uses factor graphs and
 Bayes networks as the underlying computing paradigm rather than sparse matrices
 to optimize for the most probable configuration or an optimal plan. Coupled
 with a capable sensor front-end (not provided here), GTSAM powers many
 impressive autonomous systems, in both academia and industry.
 .
 Development files

libgtsam-doc: Factor graphs for sensor fusion in robotics

 GTSAM is a C++ library that implements sensor fusion for robotics and computer
 vision applications, including SLAM (Simultaneous Localization and Mapping), VO
 (Visual Odometry), and SFM (Structure from Motion). It uses factor graphs and
 Bayes networks as the underlying computing paradigm rather than sparse matrices
 to optimize for the most probable configuration or an optimal plan. Coupled
 with a capable sensor front-end (not provided here), GTSAM powers many
 impressive autonomous systems, in both academia and industry.
 .
 Documentation

libgtsam4: Factor graphs for sensor fusion in robotics

 GTSAM is a C++ library that implements sensor fusion for robotics and computer
 vision applications, including SLAM (Simultaneous Localization and Mapping), VO
 (Visual Odometry), and SFM (Structure from Motion). It uses factor graphs and
 Bayes networks as the underlying computing paradigm rather than sparse matrices
 to optimize for the most probable configuration or an optimal plan. Coupled
 with a capable sensor front-end (not provided here), GTSAM powers many
 impressive autonomous systems, in both academia and industry.

libgtsam4-dbgsym: debug symbols for libgtsam4
python3-gtsam: Factor graphs for sensor fusion in robotics

 GTSAM is a C++ library that implements sensor fusion for robotics and computer
 vision applications, including SLAM (Simultaneous Localization and Mapping), VO
 (Visual Odometry), and SFM (Structure from Motion). It uses factor graphs and
 Bayes networks as the underlying computing paradigm rather than sparse matrices
 to optimize for the most probable configuration or an optimal plan. Coupled
 with a capable sensor front-end (not provided here), GTSAM powers many
 impressive autonomous systems, in both academia and industry.
 .
 Python library

python3-gtsam-dbgsym: debug symbols for python3-gtsam