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g2o is an open-source C++ framework for optimizing graph-based nonlinear error
functions. g2o has been designed to be easily extensible to a wide range of
problems and a new problem typically can be specified in a few lines of code.
The current implementation provides solutions to several variants of SLAM and
BA.
A wide range of problems in robotics as well as in computer-vision involve the
minimization of a non-linear error function that can be represented as a graph.
Typical instances are simultaneous localization and mapping (SLAM) or bundle
adjustment (BA). The overall goal in these problems is to find the configuration
of parameters or state variables that maximally explain a set of measurements
affected by Gaussian noise. g2o is an open-source C++ framework for such
nonlinear least squares problems. g2o has been designed to be easily extensible
to a wide range of problems and a new problem typically can be specified in a
few lines of code. The current implementation provides solutions to several
variants of SLAM and BA.
WWW: https://openslam-org.github.io/g2o.html
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