ICRA 2011 Paper Abstract

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Paper WeP105.5

Kuemmerle, Rainer (University of Freiburg), Grisetti, Giorgio (Unviersität Freiburg), Strasdat, Hauke (Imperial College London), Konolige, Kurt (Willow Garage), Burgard, Wolfram (University of Freiburg)

g2o: A General Framework for Graph Optimization

Scheduled for presentation during the Regular Sessions "SLAM III" (WeP105), Wednesday, May 11, 2011, 14:40−14:55, Room 3G

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords SLAM, Computer Vision for Robotics and Automation, Robotic Software, Middleware and Programming Environments

Abstract

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 gopt, 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 g2ooffers a performance comparable to implementations of state-of-the-art approaches for the specific problems.

 

 

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