会议专题

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 stateof- the-art approaches for the specific problems.

Rainer K(u)mmerle Giorgio Grisetti Hauke Strasdat Kurt Konolige Wolfram Burgard

University of Freiburg Department of Computing,Imperial College London Willow Garage and a Consulting Professor at Stanford University

国际会议

2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

上海

英文

3607-3613

2011-05-09(万方平台首次上网日期,不代表论文的发表时间)