G 2 o: A general framework for graph optimization

Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g 2 o, an open-source C++ framework for optimizing graph-based nonlinear error functions. Our system 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. We provide evaluations on a wide range of real-world and simulated datasets. The results demonstrate that while being general g 2 o offers a performance comparable to implementations of state of-the-art approaches for the specific problems.

Authors

• Rainer Kümmerle
• Giorgio Grisetti
• Hauke Strasdat
• Kurt Konolige
• Wolfram Burgard

Keywords

• Optimization
• Simultaneous localization and mapping
• Sparse matrices
• Jacobian matrices
• Barium
• Linear systems
• Optimization Framework
• Graph Optimization
• Computer Vision
• Error Function
• Wide Range Of Problems
• Computer Vision Problems
• Least-squares Optimization
• Bundle Adjustment
• Problem In Robotics
• Optimization Problem
• State Variables
• Linear System
• Matrix Multiplication
• Nonlinear Least Squares
• Block Diagonal
• Linear Solver
• Levenberg-Marquardt Algorithm
• Base Classes
• Cholesky Decomposition
• Preconditioned Conjugate Gradient
• Nonlinear Least Squares Problem
• 3D Datasets
• Gauss-Newton Method
• Camera Pose
• Translation Vector
• Damping Factor
• Update Step