Source: gtsam
Standards-Version: 4.7.4
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders:
 Dima Kogan <dkogan@debian.org>,
Section: science
Build-Depends:
 debhelper-compat (= 14),
 dh-sequence-python3,
 cmake,
 libboost-filesystem-dev,
 libboost-regex-dev,
 libboost-serialization-dev,
 libboost-thread-dev,
 libboost-timer-dev,
 libboost-program-options-dev,
 libboost-chrono-dev,
 libboost-date-time-dev,
 libeigen3-dev,
 libgeographiclib-dev,
 libspectra-dev,
 libsuitesparse-dev,
 libmetis-dev,
 libtbb-dev,
 pybind11-dev,
 python3-dev:any,
 libpython3-dev,
 python3-pyparsing,
 python3-numpy,
 chrpath,
Build-Depends-Indep:
 lyx,
 texlive-latex-base,
 ghostscript,
 doxygen,
Vcs-Browser: https://salsa.debian.org/science-team/gtsam
Vcs-Git: https://salsa.debian.org/science-team/gtsam.git
Homepage: https://gtsam.org

Package: libgtsam4
Architecture: any
Multi-Arch: same
Section: libs
Depends:
 ${shlibs:Depends},
 ${misc:Depends},
Pre-Depends:
 ${misc:Pre-Depends},
Description: 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.

Package: libgtsam-dev
Architecture: any
Multi-Arch: same
Section: libdevel
Depends:
 ${misc:Depends},
 libgtsam4 (= ${binary:Version}),
 libeigen3-dev,
 libboost-dev,
 libtbb-dev,
 libmetis-dev,
 libsuitesparse-dev,
Recommends:
 libgtsam-doc,
Pre-Depends:
 ${misc:Pre-Depends},
Description: 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

Package: libgtsam-doc
Architecture: all
Section: doc
Depends:
 ${misc:Depends},
 libjs-mathjax,
Description: 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

Package: python3-gtsam
Architecture: any
Multi-Arch: same
Section: python
Depends:
 ${shlibs:Depends},
 ${misc:Depends},
 libgtsam4 (= ${binary:Version}),
 ${python3:Depends},
 python3-numpy,
Provides:
 ${python3:Provides},
Description: 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
