G2o: 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 g²o, 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 g²o offers a performance comparable to implementations of state of-the-art approaches for the specific problems.

I. Introduction

A wide range of problems in robotics as well as in computer-vision involve the minimization of a nonlinear error function that can be represented as a graph. Typical instances are simultaneous localization and mapping (SLAM) [19], [5], [22], [10], [16], [26] or bundle adjustment (BA) [27], [15], [18]. 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. For instance, in graph-based SLAM the state variables can be either the positions of the robot in the environment or the location of the landmarks in the map that can be observed with the robot's sensors. Thereby, a measurement depends only on the relative location of two state variables, e.g., an odometry measurement between two consecutive poses depends only on the connected poses. Similarly, in BA or landmark-based SLAM a measurement of a 3D point or landmark depends only on the location of the observed point in the world and the position of the sensor.