Source: gtsam Standards-Version: 4.7.4 Maintainer: Debian Science Maintainers Uploaders: Dima Kogan , 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 (>= 5.0), 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 (>= 5.0), 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