Experiments¶
Repeatability of experiments is basic requirement for scientific research. We take the repeatability of our experiments very seriously and hence we release our source code and data to make our experiment repeatable.
Localization Experiment (Section V-A)¶
Setting up and running Bundler and ARToolkit is not part of this documentation so far. We have included the intermediate bundler results for comparison.:
python scripts/artk_vs_mutloc_on_tiles.py data/tiledexperiment_results/corrected_out.txt
The raw file generated from all the data collected is
data/tiledexperiment_results/out.txt but here the 0:sup:th entry is
repeated as 1:sup:st entry. So in principal you can use out.txt in
place of corrected_out.txt but all you will have to remove some other
entry because the number of experiments in
data/tiledexperiment_results/bundler-tmp/bundle/bundle.out should be same
as out.txt.
This script apart from throwing out the plots used in the paper also shows up a nice mayavi2 visualization in 3D of estimated positions by each of the algorithm.
The raw data was generated by ROS nodes
ros/mutloc_ros/nodes/tilesexperiment.py and corresspoding nodes of ROS
ARToolkit package. The node allows you to caputure raw images along with
computed position by ARToolkit and Mutual localization. Although we ran the
experiments on incoming ROS topics on the fly, and saved the corresponding
images to disk. Since we provide all these saved images (in
data/tiledexperiment_results), it should be possible to repeat the
experiments using ARToolkit and mutual localization. The logic to compute
detections for mutual localizatoin is given in
ros/mutual_ros/src/muutloc_ros/detector.py`.
Simulation experiments with noise (Section V-B)¶
This experiment can be run by simply running the following script. The path to data directory is hard coded in the script.
python scripts/noise_vs_error_on_blender.py
Reconstruction experiment (Section V-C)¶
Once camera localizations are generated by mutual localization method or bundler, we can feed them to the PMVS-2 library for semi-dense 3D reconstruction.:
python scripts/print_target_localization.py data/bnwmarker20120831/calib.yml