g2o: A general framework for graph optimization. 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. g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems (02/2011).
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Rosen, David M.: Scalable low-rank semidefinite programming for certifiably correct machine perception (2021)
- Zhao, Liang; Huang, Shoudong; Dissanayake, Gamini: Linear SLAM: linearising the SLAM problems using submap joining (2019)
- Slavcheva, Miroslava; Kehl, Wadim; Navab, Nassir; Ilic, Slobodan: SDF-2-SDF registration for real-time 3D reconstruction from RGB-D data (2018)
- Ćesić, Josip; Marković, Ivan; Bukal, Mario; Petrović, Ivan: Extended information filter on matrix Lie groups (2017)
- Calafiore, Giuseppe C.; Carlone, Luca; Dellaert, Frank: Lagrangian duality in complex pose graph optimization (2016)
- Kraft, Marek; Nowicki, Michał; Penne, Rudi; Schmidt, Adam; Skrzypczyński, Piotr: Efficient RGB-D data processing for feature-based self-localization of mobile robots (2016)
- Wilkowski, Artur; Kornuta, Tomasz; Stefańczyk, Maciej; Kasprzak, Włodzimierz: Efficient generation of 3D surfel maps using RGB-D sensors (2016)
- Wang, Heng; Huang, Shoudong; Khosoussi, Kasra; Frese, Udo; Dissanayake, Gamini; Liu, Bingbing: Dimensionality reduction for point feature SLAM problems with spherical covariance matrices (2015)
- Qayyum, Usman; Kim, Jonghyuk: Global optimization for 2D SLAM problem (2014)