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Vertigo Versatile Extensions for RobusT Inference using Graph Optimization ==================================================================== Vertigo is an extension library for g2o  and gtsam 2.0 . It provides a C++ implementation of the switchable constraints described in [2,3]. This extension enables g2o or gtsam to solve pose graph SLAM problems in 2D and 3D despite a large number of false positive loop closure constraints. In addition, a re-implementation of the max-mixture model described in  is also contained. Furthermore, Vertigo contains a number of standard pose graph SLAM datasets and a script to spoil them with false positive loop closure constraints. These datasets have been used in the evaluations  and . They can serve as a set of benchmark datasets for future developments in robust pose graph SLAM. Have fun, I hope this is useful. Hopefully, more stuff will be added to Vertigo in the future. Any comments, thoughts and patches are welcome and largely appreciated. === Contact Information === Niko Sünderhauf email@example.com http://www.tu-chemnitz.de/~niko === Licence Information === Vertigo is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. === How to compile === Dependencies: Either one or both of g2o and gtsam. Vertigo has been tested with: - gtsam 2.0.0 - g2o rev.19 (SVN from openslam.org) All other dependencies are shared with g2o or gtsam. If you can compile them, you should be fine. Compilation follows the usual steps: Create a build directory and go there. Run cmake and then make. mkdir build cd build cmake .. make This should create two libraries in the directory lib/ and the example in examples/robustISAM2. === Quick start guide using g2o === First compile the library. Then use datasets/generateDataset.py to generate a dataset spoiled with false positive loop closure constraints. cd datasets ./generateDataset.py -i manhattan/originalDataset/Olson/manhattanOlson3500.g2o -s This creates a file called new.g2o which contains the original Manhattan dataset with 100 additional random loop closure constraints. All loop closures are switchable constraints. Now lets try to solve it using g2o_viewer. g2o_viewer -typeslib ../lib/libvertigo-g2o.so new.g2o You should see a plot of the initial map, with the switchable loop closure constraints in red. Increase the number of iterations to 30, keep Gauss-Newton activated, and hit "Optimize". The switchable constraints turn black as they are "switched off" and g2o converges towards the correct map. Let us explore some more options of the generateDataset.py script. In addition to the call above, add the option -n 500. Now we have 500 outliers instead of 100 as before. Add an additional -l to get local outliers. Use -n 10 -g 50 to get 10 groups of 50 outliers each. For comparison, remove the option -s. Now you have the non-switchable default constraints, which result in a useless map. The option -m instead of -s gives you the max-mixture model described in . Notice that these constraints are originally drawn in orange and turn black when the alternative null-hypothesis constraint is selected. The option --help tells you more about the possible command line options. Especially notice that you will want to provide a reasonable information matrix for the false positive loop closure constraints (usually compatible with the matrix given for the true positive constraints), using the --information option. You can either provide the full upper triangular form, e.g. --information=1,0,0,1,0,1 (for a 2D dataset) or just specify a single Value that will be used for all diagonal entries of the matrix, e.g. --information=1 === Acknowledgements === The datasets were provided by / reproduced from: Manhattan/Olson provided by Edwin Olson Manhattan/g2o released as part of g2o Intel released as part of g2o Sphere2500 released as part of iSAM1 (Michael Kaess) City10000 released as part of iSAM1 (Michael Kaess) === References ===  Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, and Wolfram Burgard: g2o: A General Framework for Graph Optimization, IEEE International Conference on Robotics and Automation (ICRA), 2011 Available online: http://openslam.org/g2o.html  Sünderhauf, N., Protzel, P. (2012). Switchable Constraints for Robust Pose Graph SLAM. Proc. of IEEE International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.  Sünderhauf, N. (2012). Robust Optimization for Simultaneous Localization and Mapping. PhD Thesis, Chemnitz University of Technology.  Olson, E. and Agarwal, P. (2012). Inference on networks of mixtures for robust robot mapping. Proc. of Robotics: Science and Systems (RSS), Sydney, Australia.  GTSAM2.0 https://collab.cc.gatech.edu/borg/gtsam/