This is an implementation of the algorithm given in [1] for Pose Graph Optimization. The code is in Julia.
Three initialization techniques have been implmented: (a) odometry, (b) spanning tree, and (c) chordal relaxation.

• Follow the instructions here to install Julia, Atom, and Juno: https://docs.junolab.org/stable/man/installation/
• Open up the examples.jl file under SparsePoseAdjustment directory in Atom. Execute the instructions from the beginning (similar to Jupyter Notebook).
• The code uses datasets in g2o format. To test the code on a new dataset, specify the location of the file similar to examples shown in examples.jl.

Following are the optimized pose graphs, using chordal relaxation initialization technique.

(a) City10000 dataset                 (b) CSAIL dataset.

(c) Manhattan dataset                 (d) Kitti dataset.

[1] Konolige, K., Grisetti, G., Kümmerle, R., Burgard, W., Limketkai, B., & Vincent, R. (2010). Efficient Sparse Pose Adjustment for 2D mapping. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 22-29. [2] Juno: https://docs.junolab.org/stable/man/installation/